{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dfsu Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demonstrate some features of `dhitools` to read and analyse DHI MIKE `.dfsu` files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from dhitools import dfsu\n",
    "from dhitools import mesh\n",
    "from dhitools import units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tl;dr - snippets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concise list of the main functions used in this example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "..\\dhitools\\_gridded_interpolate.py:107: RuntimeWarning: invalid value encountered in less\n",
      "  interp_z[np.any(weights < 0, axis=1)] = fill_value\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "area_f = 'data/area.dfsu'\n",
    "inundation_f = 'data/inundation.dfsu'\n",
    "area = dfsu.Dfsu(area_f)\n",
    "inundation = dfsu.Dfsu(inundation_f)\n",
    "\n",
    "# Data for single time step either node or element\n",
    "current_speed_t500 = area.item_node_data('Current speed', tstep_start=500)\n",
    "\n",
    "# Data for range of time step either node or element\n",
    "surface_elevation_t400_to_t450 = area.item_element_data('Surface elevation', tstep_start=400, tstep_end=450)\n",
    "\n",
    "# Max amplitude either from inundation file or area output (which is temporal)\n",
    "max_amplitude_1 = inundation.max_ampltiude() # Method 1\n",
    "max_amplitude_2 = area.max_item('Surface elevation', node=True) # Method 2\n",
    "\n",
    "# Regular grid of data\n",
    "mesh_f = 'data/example_mesh_no_elevation.mesh'\n",
    "m = mesh.Mesh(mesh_f)\n",
    "area.grid_res(res=500)\n",
    "mask = m.mask()\n",
    "bm = area.boolean_mask(mask, res=500)\n",
    "grid_cs_t500 = area.gridded_item(item_name='Current speed', tstep_start=500, res=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input .dfsu file: data/area.dfsu\n",
      "Num. Elmts = 1164\n",
      "Num. Nodes = 709\n",
      "Mean elevation = -152.855828060649\n",
      "Projection = \n",
      " PROJCS[\"GDA_1994_MGA_Zone_56\",GEOGCS[\"GCS_GDA_1994\",DATUM[\"D_GDA_1994\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"False_Easting\",500000],PARAMETER[\"False_Northing\",10000000],PARAMETER[\"Central_Meridian\",153],PARAMETER[\"Scale_Factor\",0.9996],PARAMETER[\"Latitude_Of_Origin\",0],UNIT[\"Meter\",1]]\n",
      "\n",
      "\n",
      "Time start = 1/01/2017 12:00:00 AM\n",
      "Number of timesteps = 916\n",
      "Timestep = 50.0\n",
      "\n",
      "\n",
      "Number of items = 5\n",
      "Items:\n",
      "Surface elevation, unit = m, index = 0\n",
      "U velocity, unit = m/s, index = 1\n",
      "V velocity, unit = m/s, index = 2\n",
      "Current speed, unit = m/s, index = 3\n",
      "Current direction, unit = rad, index = 4\n"
     ]
    }
   ],
   "source": [
    "area.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the following sections for more details on the  above commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "1. Load and inspect `.dfsu` outputs\n",
    "2. Plot various `.dfsu` output items\n",
    "- Calculate maximum amplitude from inundation output\n",
    "- Create new `.dfsu` files\n",
    "- Convert `dfsu` data from unstructured to regular grid ready for raster export"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "## 1. Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have two `.dfsu` outputs from the same model run:\n",
    "\n",
    "- `area.dfsu` contains area outputs over the model spatial and temporal domain for various items such as surface elevation, current speed and current direction\n",
    "- `inundation.dfsu` contains an inundation output. This is an output format from MIKE21 HD which provides aggregate stats over the entire model run for maximum water level, current speed and times that these occurred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_f = 'data/area.dfsu'\n",
    "inundation_f = 'data/inundation.dfsu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = dfsu.Dfsu(area_f)\n",
    "inundation = dfsu.Dfsu(inundation_f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks at summaries of these files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input .dfsu file: data/area.dfsu\n",
      "Num. Elmts = 1164\n",
      "Num. Nodes = 709\n",
      "Mean elevation = -152.855828060649\n",
      "Projection = \n",
      " PROJCS[\"GDA_1994_MGA_Zone_56\",GEOGCS[\"GCS_GDA_1994\",DATUM[\"D_GDA_1994\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"False_Easting\",500000],PARAMETER[\"False_Northing\",10000000],PARAMETER[\"Central_Meridian\",153],PARAMETER[\"Scale_Factor\",0.9996],PARAMETER[\"Latitude_Of_Origin\",0],UNIT[\"Meter\",1]]\n",
      "\n",
      "\n",
      "Time start = 1/01/2017 12:00:00 AM\n",
      "Number of timesteps = 916\n",
      "Timestep = 50.0\n",
      "\n",
      "\n",
      "Number of items = 5\n",
      "Items:\n",
      "Surface elevation, unit = m, index = 0\n",
      "U velocity, unit = m/s, index = 1\n",
      "V velocity, unit = m/s, index = 2\n",
      "Current speed, unit = m/s, index = 3\n",
      "Current direction, unit = rad, index = 4\n"
     ]
    }
   ],
   "source": [
    "area.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input .dfsu file: data/inundation.dfsu\n",
      "Num. Elmts = 1164\n",
      "Num. Nodes = 709\n",
      "Mean elevation = -152.855828060649\n",
      "Projection = \n",
      " PROJCS[\"GDA_1994_MGA_Zone_56\",GEOGCS[\"GCS_GDA_1994\",DATUM[\"D_GDA_1994\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"False_Easting\",500000],PARAMETER[\"False_Northing\",10000000],PARAMETER[\"Central_Meridian\",153],PARAMETER[\"Scale_Factor\",0.9996],PARAMETER[\"Latitude_Of_Origin\",0],UNIT[\"Meter\",1]]\n",
      "\n",
      "\n",
      "Time start = 1/01/2017 12:00:00 AM\n",
      "Number of timesteps = 0\n",
      "Timestep = 5.0\n",
      "\n",
      "\n",
      "Number of items = 5\n",
      "Items:\n",
      "Maximum water depth, unit = m, index = 0\n",
      "Time at maximum water depth, unit = sec, index = 1\n",
      "Maximum current speed, unit = m/s, index = 2\n",
      "Time at maximum current speed, unit = sec, index = 3\n",
      "Duration above threshold, unit = sec, index = 4\n"
     ]
    }
   ],
   "source": [
    "inundation.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `summary()` method provides a quick glimpse of key information within the `dfsu` file. The listed `items` are various outputs within these files and we use the item names to read them from the `dfsu` object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Items can be read in to numpy arrays, for single or a range of timesteps where applicable at either node or element coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Node and element data for all timesteps:**\n",
    "\n",
    "If `step_end` = -1, will get read all data from `tstep_start` to end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_surface_elevation_all = area.item_node_data(item_name='Surface elevation', tstep_start=0, tstep_end=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "element_surface_elevation_all = area.item_element_data(item_name='Surface elevation', tstep_start=0, tstep_end=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(709, 917)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_surface_elevation_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1164, 917)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element_surface_elevation_all.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data for single timestep**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(709, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_speed_t500 = area.item_node_data('Current speed', tstep_start=500)\n",
    "current_speed_t500.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns current speed for timestamp 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data for range of timesteps**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1164, 51)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface_elevation_t400_to_t450 = area.item_element_data('Surface elevation', tstep_start=400, tstep_end=450)\n",
    "surface_elevation_t400_to_t450.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data for single element**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data for a single mesh element, or list of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 917)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface_elevation_ele300 = area.item_element_data('Surface elevation', tstep_end=-1, element_list=[300])\n",
    "surface_elevation_ele300.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAEWCAYAAAA+f1laAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd8ZGd1P/7Pmd400qivykrb197msutehCHGxtjO16YYUjAhIYQvgQQShxJM+0EIBEIKCZhvCCVgY1xiY9xwkeva27y9F2nV64w0vT6/P+69o5E05c7MnRmV83699LJ3ZjTzSHck3XPPec4hIQQYY4wxxhhjjC0/ukovgDHGGGOMMcZYZXBAyBhjjDHGGGPLFAeEjDHGGGOMMbZMcUDIGGOMMcYYY8sUB4SMMcYYY4wxtkxxQMgYY4wxxhhjyxQHhIwxxpKIqImIXiYiLxF9p9LrAQAi6iGid1TgdZ8iog+V4HmtRPQbIpoiol9r/fxpXq8i3z/GGGOLAweEjDG2iBHRNUT0uhxcTBLRa0S0o4in/CiAcQBOIcRnNFrmgkdEXyai/0m9TQhxsxDipyV4ufcAaAJQJ4R4bwmef8EjoruJ6NUcj/kJEUWIyJfyoU+5/+1EdJyIAkT0IhF1pNxnJqIfE9E0EQ0T0adL+fUwxthixgEhY4wtUkTkBPAEgH8DUAugFcBXAIQLeC4iIh2ADgBHhRBCy7WyWToAnBRCxPL9RCIylGA9C9m3hBCOlI84ABBRPYBHAHwR0nt/D4BfpXzelwGsg/S9fhuAe4joprKunDHGFgkOCBljbPFaDwBCiPuFEHEhRFAI8awQ4iAwP+tFRJ1EJJSggoi6iejrRPQagACAnwH4EKSTZx8RvYOILiOinUTkIaIhIvp3IjKlPOcmIvqdnJ0cIaLPy7friOizRHSGiCaI6EEiqs30hRDRu4lov/w6rxPR1gyPy/i8RPQ0EX1izuMPENEd8v//CxH1yVmjvUR0rXz7TQA+D+D98td9IOX786cpr/v3RNRLRKNE9DMiqp7zff0QEZ0nonEi+kKG9X8FwL0pr/URlc/9ESI6D+CFEn7/lNf6sPx9chPRx4hoBxEdlJ/73+c8358Q0TH5sc/MydIJ+fNPyfd/X77wcAGAHwC4Uv4eeNKtNYc7ABwRQvxaCBGCFABuI6KN8v1/DOBrQgi3EOIYgB8BuLuA12GMsSWPA0LGGFu8TgKIE9FPiehmInIV8Bx/BKlMtArAhwH8AjNZmecAxAH8NYB6AFcCeDuAjwMAEVUBeA7A0wBaAKwF8Lz8vJ8E8PsArpfvcwP4froFENElAH4M4M8B1AH4IYDHicic5uHZnveXAD6Q8rwXQsoQ/Va+aTeAiyBllH4J4NdEZBFCPA3gGwB+JX/d29K87t3yx9sArAbgAPDvcx5zDYAN8vfoXjnwmUUI8aU5r/VfKp/7egAXAHjn3OfU8PunuBxSdu39AL4H4AsA3gFgE4D3EdH18uv+PqRA+g4ADQBeAXD/nOd6N4AdALYBeB+Ad8oB2scA7JS/BzVp1qn4uHyxYS8R3Zly+yYAB5R/CCH8AM4A2CT/HLSk3i///6Ysr8MYY8sWB4SMMbZICSGmIQUhAlIGZIyIHieipjye5idCiCNCiJgQIprmNfYKId6Q7++BFGxcL9/9bgDDQojvCCFCQgivEOJN+b4/B/AFIUS/ECIMKYPzHkpf8vhnAH4ohHhTznT+FFLZ6xVpHpvteR8FcFFKluoPADwiPw5CiP8RQkzIX8t3AJghBXBq/AGA7wohzgohfAA+B+CuOV/PV+Qs7QFIAUi6wLLQ5/6yEMIvhAim+Xytvn+Kr8nH81kAfgD3CyFGhRADkIK+i1Oe6x+EEMfk8tdvYPb3HwC+KYTwCCHOA3gRUkCu1r9CCkwbIZWG/oSIrpbvcwCYmvP4KUgXNhwp/557H2OMsTk4IGSMsUVMPhm/WwjRBmAzpMzI9/J4ir5sdxLReiJ6gqTGHNOQTvrr5bvbIWVl0ukA8KhcZugBcAxStjFdsNoB4DPKY+XHt8tfi+rnFUJ4IWUD75IfexekjKfytXxGLm+ckj+3OuVryaUFQG/Kv3sBGOZ8PcMp/x/ATGCixXNnO06afP9SHjOS8v/BNP9Wvq4OAP+S8lyTAAjSXlZFod8TCCH2pQTwT0I6lnfId/sAOOd8ihOAV74Pc+5X7mOMMTYHB4SMMbZECCGOA/gJpMAQkLI7tpSHNKf7tBxP+58AjgNYJ4RwQioRJPm+PgBrMnxeH4CbhRA1KR8WOcuU7rFfn/NYmxBibvmhmue9H8AHiOhKAFZIWSnI+wX/DlLZoksuU5xK+VpyfR8GIQVAipUAYpgdLBVKzXNnW5+W37989AH48znPZRVCvK7icwtpWiQwc7yOICUDS0R2SO/FI0IIN4AhzM7QbpM/hzHG2BwcEDLG2CJFRBvlrFeb/O92SHvo3pAfsh/AdUS0Um5S8rkCXqYKwDQAn9yw4y9S7nsCQDMR/RVJbf6riOhy+b4fAPi6Uj5IRA1EdHuG1/gRgI8R0eVy0xE7Ed0i71GcK9fzPgkpuPoqpH16iZSvIwZgDICBiO7F7AzSCIBOkjqtpnM/gL8molVE5MDMPsC8O4WW4Lm1/P7l4wcAPkdEm+TnqiYitWM0RgC0UUqDormI6D1E5CCpEc6NAP4QwOPy3Y8C2ExEdxKRBVKjnoPyRRFAapD090Tkkt+3fwbpYgljjLE5OCBkjLHFywupAcibROSHFAgeBvAZABBC/A5SK/6DAPZCCuDy9TcAPii/1o+Q0tpfLtH8PQC3QioNPAWpMQoA/Aukk/dnicgrr+1ypCGE2APphP3fITU5OY3MHSGzPq+8L+4RSE1Qfpnyec8AeApSI55eACHMLsNUBsRPENG+NK/7YwA/B/AygHPy5/9lhjXmq6jn1vL7lw8hxKMA/hHAA3I58WEAN6v89BcgZeyGiWg8w2M+BWAAgAfAtwH8mRCiW37tMQB3Avg6pK/5csyUCgPAlyCVM/cCeAnAt+XmQYwxxuYgHjXFGGOMMcYYY8sTZwgZY4wxxhhjbJnigJAxxhhjjDHGlikOCBljjDHGGGNsmeKAkDHGGGOMMcaWKUOlF6C1+vp60dnZWellzOP3+2G32yu9DFYifHyXNj6+Sxcf26WNj+/Sxsd36eJjW7y9e/eOCyEa1Dx2yQWEnZ2d2LNnT6WXMU93dze6uroqvQxWInx8lzY+vksXH9uljY/v0sbHd+niY1s8IupV+1guGWWMMcYYY4yxZYoDQsYYY4wxxhhbpjggZIwxxhhjjLFligNCxhhjjDHGGFumOCBkjDHGGGOMsWWKA0LGGGOMMcYYW6Y4IGSMMcYYY4yxZYoDQsZYwd4678ajb/UjGk9UeimMMcYYY6wAS24wPWOsPB7bP4BPPbAfAPD4/kH894cvq/CKGGOMMcZYvjhDyBjLmzcUxVd/cxQXtdfgkzesxYsnxnB4YKrSy2KMMcYYY3nigJAxlrcnDg5hwh/BvbdeiI9cuxo2kx6/3HW+0stijDHGGGN54pJRxljeHt7bj3WNDlzcXgMiwmWrarG3x13pZTHGGGOMsTxxhpAxlpdxXxh7et24bVsLiAgAsKW1GqdGvQhG4hVeHWOMMcYYywcHhIyxvLxxdgIAcM26+uRtW1qrkRDA0aHpSi2LMcYYY4wVgANCxlhedp6ZgMNswJbW6uRtW9qk/z/U76nUshhjjDHGWAE4IGSM5WV3zyS2d7pg0M/8+mh2WmA36dE7GajgyhhjjDHGWL44IGSMqRaKxnF61IetKdlBACAitNRYMegJVmhljDHGGGOsEBUNCInoJiI6QUSnieizGR7zPiI6SkRHiOiX5V4jY2zGyREvEgK4YIVz3n1SQBiqwKoYY4wxxlihKjZ2goj0AL4P4PcA9APYTUSPCyGOpjxmHYDPAbhaCOEmosbKrJYxBgBHB6WmMZkCQh5OzxhjjDG2uFQyQ3gZgNNCiLNCiAiABwDcPucxfwbg+0IINwAIIUbLvEbGWIpjQ9Owm/RYWWubd1+by4oJfwShKI+eYIwxxhhbLCo5mL4VQF/Kv/sBXD7nMesBgIheA6AH8GUhxNNzn4iIPgrgowDQ1NSE7u7uUqy3KD6fb0Gui2ljuRzf3SeDaLICL7/80rz7poZiAID/ffYlNNuX1vbk5XJ8lyM+tksbH9+ljY/v0sXHtrwqGRBSmtvEnH8bAKwD0AWgDcArRLRZCDGrt70Q4j4A9wHA9u3bRVdXl+aLLVZ3dzcW4rqYNpbL8f3CGy9gxyoXurounnef9ewE7jv4BlrXb8G16xoqsLrSWS7HdzniY7u08fFd2vj4Ll18bMurkpfx+wG0p/y7DcBgmsc8JoSICiHOATgBKUBkjJVZKBrH4FQQnfX2tPc3OS0AgNHpcDmXxRhjjDHGilDJgHA3gHVEtIqITADuAvD4nMf8L4C3AQAR1UMqIT1b1lUyxgAAfZMBCAF01qUPCOurzACACT8HhIyxxcXtjyAY4f3PjLHlqWIBoRAiBuATAJ4BcAzAg0KII0T0VSK6TX7YMwAmiOgogBcB/K0QYqIyK2ZseTs37geAjBlCu0kPs0GHcV+knMtijLGi3L/rPHZ8/Tlc9vXn8MLxkUovhzHGyq6SewghhHgSwJNzbrs35f8FgE/LH4yxCuqdCAAAOuvmdxgFpOH09Q4zxn2cIWSMLQ6RWALf+O0xXLLSBX8khv/7i7fw/GeuR0uNtdJLY4yxsllarQAZYyXT7w6gymJAjc2U8TF1DhMmOEPIGFskXjszDm84ho91rcb3P3gJgtE4Hj8wt50BY4wtbRwQMsZUGfCE0JrjqjlnCBlji8lzR0fgMBtw9dp6dNbbsa29Bo/v54CQMba8cEDIGFNl0BPMWUZVZ+cMIWNs8Tg6NI3NrU6YDXoAwI0XNuHo0DSmQ9EKr4wxxsqHA0LGmCoDniBaaixZH1PnMGPCH4a0/ZcxxhYuIQROj/qwttGRvG1Ng9Q067y8Z5oxxpYDDggZYzn5wjFMBaNorUnfUEZR7zAhGheYDsXKtDLGGCvMmDcMbyiGtQ0zAeHKWikg7JnwV2pZjDFWdhwQMsZyGvIEAUBFhlBqOMP7CBljC93pUR8AYG1jVfK2DrmLci9nCBljywgHhIyxnPrlgDBXUxmlA6knwPtvGGML25kxJSCcyRDazQY0VJnRyxlCxtgywgEhYyynQSUgdGUPCF3JgJAbyzDGFrYBTwhGPaHJaZ51e0etDT2cIWSMLSMcEDLGchr0BKHXERqrspeM1liNADhDyBhb+Ea9ITQ4zCCiWbe319ow4A5WaFWMMVZ+HBAyxnIa9ITQ7LRAr6Osj0tmCIMcEDLGFrYxbxgNzvkXuRqqpHmq3C2ZMbZccEDIGMtpwB3MWS4KAFUWA4i4ZJQxtvCNecNocJjn3V7vMCEcS8Ab5m7JjLHlgQNCxlhOA55gzoYyAKDTEaqtRi4ZZYwteOO+MBqq5geEym3jXu6WzBhbHjggZIxlFU8IDE+Hco6cULhsJrg5Q8gYW8Bi8QQm/BE0pgkI6+Ws4RgHhIyxZYIDQsZYVqPeEOIJgRYVGUIAqLYaMcV7CBljC9iEPwIhkD1D6OMLW4yx5YEDQsZYVkNTIQDAimp1GcIam5EzhIyxBU3J/qULCGcyhKGyrokxxiqFA0LGWFaj09KJU66REwqXzcR7CBljC5oSENanaSrjspmg1xFnCBljywYHhIyxrEblq+SNzvknTulUW42Y4oCQMbaAKVUMtXbTvPv0OkKt3cR7CBljywYHhIyxrEamQ9DrCHV2dQFhjc0IbziGWDxR4pUxxlhhlH3ONVZj2vvrHdIsQsYYWw44IGSMZTU6Lc3qyjWUXuG0SCdY3hDP8GKMLUxKWbszQ0BYZzdhkvdCM8aWCQ4IGWNZjXjDqstFgZkTrOkQl40yxhamqWAUVRZDxgtdLrsJbj8HhIyx5aGiASER3UREJ4joNBF9Nsvj3kNEgoi2l3N9jDFgdDqkuqEMIO0hBMCjJxhjC9ZUMJr8XZVOrc2ISQ4IGcsqnhB48tAQ3jg7UemlsCJVLCAkIj2A7wO4GcCFAD5ARBemeVwVgE8CeLO8K2SMAcCoN4ymfDKEFgMAYDrIJaOMsYVpKhhFjS1LQGg3YzoUQ5T3QjOW0beeOY6P/2If7rrvDTy0t7/Sy2FFqGSG8DIAp4UQZ4UQEQAPALg9zeO+BuBbAHggEGNlFoklMOmP5JUh5JJRxthC5wlEsmcI7dJ9PFOVsfRGp0O47+WzuGXrCly2qhZffvwI/91fxCoZELYC6Ev5d798WxIRXQygXQjxRDkXxhiTjMld9vLJEHLJKGNsoZsKRlFjnT9yQuGSx1G4/fx7jLF0njo8DCGAv3r7OnzxlgvhC8fwv28NVHpZrECGCr52up3cInknkQ7APwO4O+cTEX0UwEcBoKmpCd3d3dqsUEM+n29BrotpY6ke39OeOABgpPckugNnVX1OMCb9GL915DhWqPychW6pHl/Gx3apy3R8x6YCaDeHMx77vgnpd98Lr+3CUJ2+hCtkxeCf38q5f1cQrQ7CwLG9AIBOpw7/74VjWBnu0eT5+diWVyUDwn4A7Sn/bgMwmPLvKgCbAXQTEQA0A3iciG4TQuxJfSIhxH0A7gOA7du3i66urhIuuzDd3d1YiOti2liqxzd0eAh4Yx/eftUObG6tVvU5QgjoX3gKDS0r0dW1scQrLI+lenwZH9ulLt3xFUIg+LunsHFNR8bfUc3D0/jH3a9g5boL0bV1RRlWygrBP7+VEU8I/MXzz+D9O1aiq2sTAOD9OIN/fPo4LrzkCjQ61W8zyYSPbXlVsmR0N4B1RLSKiEwA7gLwuHKnEGJKCFEvhOgUQnQCeAPAvGCQMVY6o16lZFT9L3cigtNi4KYyWZwY9uL//mIffvjSGQghcn8CY0wzwWgc0bjI3lTGJpWM8ixCxuY7M+ZDMBrHlpQLxdeuqwcAvHp6vFLLYkWoWEAohIgB+ASAZwAcA/CgEOIIEX2ViG6r1LoYYzNGpkPQ6wh19sx7bdJxWo28hzCLf3r2BH57aAj/8NRx/M+b5yu9HMaWFWUofbamMsoewkkfB4SMzXWofwoAsKVtJiC8cIUTtXYTXj3FAeFiVMmSUQghngTw5Jzb7s3w2K5yrIkxNmNkOowGhxm6DMObM3FajNxtLIO+yQCeOzaCT7xtLfb0TuL7L5zGH1y2Mu/vMWOsML6wVL3gtGQOCI16HaosBu4yylgahwamYDXqsabBkbxNpyNs73Bh33l3BVfGCpU1ICSiO1Q8R0gO7BhjS0y+MwgV1VYjpjlDmNbOsxMQAvj9i1uxttGBv/rVfuw778b2ztpKL42xZcEbkgJChyX7NfE6uwkTPJyesXlOjXqxrskB/ZwLmRetrMGzR0fg9keSWXa2OOTKEP4IwGNI3xFUcR3mZPkYY0vD6HQIbS5b3p/ntBowNBUswYoWv8MDU3CYDVhdb0eT0wyTQYdnjgxzQMhYmSgZQoc5+ymQy26CmwNCxuY5O+bHFavr5t1+UXsNAGB/vwdv29BY7mWxIuQKCJ8SQvxJtgcQ0f9ouB7G2AIy6g3j0g5X3p8nlYxyU5l0Dg1M4cIWJ3Q6QpXFiAuaq3BkcLrSy2Js2fDJv5uqcmQIa20mDE2FyrEkxhaNQCSGoakQ1jTY5923ta0GRMCBvqUdEP709R6cHvXhb27cgOoszakWk6xNZYQQf5jrCdQ8hjG2+ERiCUz6I3l1GFVwyWh6sXgCx4amZ3Vm29jsxLGh6SXdbbRn3I8XT4wiEOGLBKzyfGHpd1OuDGGt3YRJzhAyNsvZMT8AYHXK/kGFw2zAqjo7jg95y72ssnn68BC+9PgR/PyNXnzxscOVXo5mVDWVISI9gFsAdKZ+jhDiu6VZFmOs0sZ80siJxqr89xA6rUaEYwmEonFYjDzUWTHoCSEUTWBDU1XytgtWVOFXe/ow5g1rMrtpoXlobz/ueegAEkLqQvfIx6/i9wSrKLV7CGvtJkwGIhBCQJ6HzNiyd3ZcCQjnZwgBYENzFY4NLc2qFyEE/vX501jf5MCmlmq8cHwUiYRYEk3h1I6d+A2AuwHUQRoYr3wwxpaokWmpVKqQDKFTPtHiTqOzDXikfZVtLmvyto0rnACAY8NL74rq8FQIX378CLZ31uKbd2zB0aFp/PCls5VeFlvmlD2EdlPuPYSRWAKBSLwcy2JsUeiRA8LOuvQB4cZmJ3onA0uyImTfeQ+ODk3j7qtW4br19ZgKRnF0iQS/asdOtAkhtpZ0JYyxBWVUDggbCswQAsB0MIZGvnSUpASELTUzAeG6Rqns5vSoD9evb6jIukrlv149i2A0jm+/Zys66uz47aEhPLyvH598+1rOuCwg0XgCCSFgNpQnc7vr3CQe3tuPOy9tw2Wryt9MyReKwWbSz+uQOFetMovQH4E9R3kpY8tF32QATU5zxkqPDc1VEAI4OeJLNplZKp47NgKDjnDrthXwh6ULRW+cncDmlG0gi5XaDOFTRHRjSVfCGFtQRr1SyWhBGUI5IOTh9LMNygFhc/XM97TWboLZoMPwEuvKGojE8MCuPty8uRkd8pXkW7e24PxkAAfkocZsYbj7v3dhy5eexVOHhkr+WtF4An/38EH8ak8fPvKT3fBUYM6fLxzLuX8QkJrKAOB9hIyl6HcHs3Yfv2CFdBX4+BLJnKV69dQ4Ll5ZgyqLEc3VFjRUmXFiiVT3qA0I3wDwKBEFiWiaiLxEtPSONGMsaWQ6BL2OUFfALCFl4DOXjM426AmioWr2lVUiwopqy5LrZvjSiTF4wzF88LKVydtu3NQEHQEvHBup4MpYqt4JP147PYFIPIFf7jpf8td79sgIzo378Tc3rocvEsN/vXqu5K85lzccy7l/EEByjhoHhIzN6PcEZm17mKvdZYPNpMfxJRIoKdz+CA4PTuGatTOVPGsa7Dgz5qvgqrSjNiD8DoArAdiEEE4hRJUQwlnCdTHGKmx0OowGh7mgzdLVVnkPIWcIZxnwBGeViyqaqy0YXmIB4dNHhuGyGWeVBNbYTNjaVoNXTo9XcGUs1WP7B0EEvOOCRuzrdSMWT5T09Xb3TMJm0uNj16/B9esb8L/7B8reYdcXiqFKRYawjgNChKJxjMsNxhiLxRMY9ISyBoQ6HWF9UxWODy+tvNHhwSkIAezonBnFtabBgTNj/iXRJVxtQHgKwGGxFL5ixpgqI94wmpz57x8EUvcQckCYasATRGvN/BLclmrrksoQCiHQfWIMN2xsgkE/+8/MtevqcaDPw+XEC8Srp8axtbUat13UCn8kXvKr+m/1ebCltRoGvQ43b25G32Sw7HM4fXlmCN0VKGtdCPadd+PSr/0O2/+/5/DjCmRyWW5ufwT3PnYY9zx0INkIrpSGp0OIJ0TWklEA2NhchePD3iURKCmU0tANzTONEdY0ODAVjGJiCVw0UhsQDgHoJqLPEdGnlY9SLowxVlmj0yE0VBU2BmGmZHTpdRkrxshUKO2ezOZqC0bkP7RLwfnJAKaCUWxPuZKquGZtPRIC2HlmogIrY6nCsTj293uwo7MWF8vNHw6WcH9nOBbHscFpXLRSeq3fu7AZRMDzx0ZL9prp+ELq9hA6LQYYdLQkTvbyFYkl8KkH3oLLbsI7LmjEV584ildOjVV6WSyFEAKffOAt3L/rPB7bP4i/fehgyV+z3z2/U3Y6G5ur4AlEMTK9dLLLx4e9qHeYUeeYuVC+NqUp3GKnNiA8B+B5ACbw2AnGloXRIjKEFqMeJoOOM4QpQtE4/JE46h3zv6crqi2IJQQmlkhp1uEBKeOzuWV+57WLV7pgM+nx6mk+uay0wwNTiMQS2N5ZixXVFugIJW1udHLYh0g8gW1tUkBYazdhQ1MV9vROluw105GayhhzPo6I4LKb4F6GAeGLJ0bRNxnEl2/dhH/7wCVY02DHlx4/sqQyPovdM0eG8cqpcdz77gvx8a61ePnkWHIkRKkMTc3vlJ3OOnnW7lLZXwdIGUKlYY5iVb3UMK13orTf93JQFRAKIb6S7qPUi2OMVUYklsCkP4LGAjOEAFBtNXJTmRTKPqTaNE16VlRLf1wHl0jZ6OHBKRh0hPXNjnn3mQw6XLG6Dq+e4n2Elba7xw0A2N7pgkGvQ73DjOESlp2dnwwAmDmJUl77rfOesmbHvaEoqlSUjAJSp9HluIfw4b39aKgyo2tDA6wmPT7etRZnx/x481x5g3eW2X0vn0VHnQ0fvLwDd13WDgB44uBgSV9T2drQnKP7uPIzfq7EAWq5JBICp0a9WN80OyBcUW2BXkfJzOliljUgJKIv53oCNY9hjC0uYz5l5ERhGUJAKrfifWIzsgWE9fKsx6WSITwyOI11TVUZ59pds7YePRMB9MkBAquMPT1urK63J7PWUuly6d6DfW7peKeWm23vqIUvHCtbAwohBPyROGwmdTMXa+3LLyCMxRN45dQ4bt7cnNwDfMvWFaiyGPDovoEKr644nkBkSVyoPNQ/hX3nPfjwVZ3Q6whNTgtWN9hLPtJnZCqEKrMh51zOZqcFZoOu5BnLchn3hRGKJtBZN3vvpEGvw4pqy5L4W5brEtmf5hgvQQDuAvBlzVbEGKs4ZSh9YzEBodWI6SDvIVQonfrqHfMDQqWb4VLZq3Rm1DerE9tc162vBwC8enocH0gZS8HKJ5EQ2Ns7iXdc0JS8rclZ2hObvskAamxGVFlmyjWVwdVHBqaxKU2JsdbCsQTiCaF60Hyt3YRjS6xbYi7Hh70IRuPY0TnTIdhi1GNHZy32nXdXcGXFefzAIO556AASAvj2e7bi9otaK72kgv16bx/MBh3uuLQtedvW1mrsPFvavdnD06FZc3Qz0ekInXX2JZMh7JdnCLem2TvZ7rKhb6lnCAH8CLP3DM79cMiPYYwtIUqWgEtGtTOTIZwfZNfJQeKEb/EHhKFoHAOeIFY3zC8XVaxpcKDZaeGy0Qo6O+6DOxCdddLf7LSUtGS0zx1E+5zuhCtrbbAa9WULuoKROACozhC67MZlt4dwT49gB9r7AAAgAElEQVRUFnppx+yLOlvbqnF6zAdfePFd6POHY7j3scPY2OzEphYn/v7Rw2XpylkK4Vgcj+0fxE2bm5MN3ABgS1sNRqbDJf26hqfDqgJCAOist+HcEthbBwADcsDXWjO/u2qby7r0M4S8T5Cx5WnMK/1BSdcRUy2nxbhkrg5qIVvJqM1kgMWow6R/8ZeMKsc8dZ/YXESEq9fW48UToxBCgCj/WZesOPv7pNKyi+WOn4BUMuoJRBGKxmExqguY8tHvDmBj8+w9ODodYUNzFY4PlWeItT8iBTN2k9oMoRmeYBTxhIC+gJmsi9G+8x40Oy3zGodsa6+BEFK54pVr6iq0usI8uKcPnkAUX7r7QrhsJrzjuy/hBy+dwZdu3VTppeXt+WOjmApG8Z6U7CAgBewAcGRwqqi/3dkMTwWxrrEh9wMBrKp34IXjo4jFE/PGDy02Ax6lmc7872t7rQ2j3nDJfm+Wy+I+QoyxkhiZDkOvo2QpYyGcVgN3GU0x7ovAqCc4MzSzqLObl0TJqBIQrm7IHBAC0nDfSX9k0V00EELggV3n8blHDi3qzNHRwWlYjLpZmdxGeS9rKTIMiYRAf5oMIaDMLJsuSwfLgJIhNKvcQ2gzQghp79lycXx4GptbnfNuV7rDHh4o7T61Uug+MYZ1jQ5cvNKFzno7br+oFffvOo+pwOL7G/XrPX1YUW3BVWvqZ92+OtnIpTTZqlg8gTFvOGdDGcWqehuicZEMphazAXcQTothVrm7or1WunCy2L9ODggZY/OMTIfQ4DBDV8QVcalkNMZtymWT/jBq7aaM2bCF1LzCG4pi33k3ovFE3p+rJkMIIDmjcE/v4tqT9Mtd5/HZRw7h/l3n8a1njpfkNZSg87MPHyxZed6RwSlsbHbOynopWYUxr/aZ6jFfGJFYAm216QNCdyBaktedyx/OL0OoDKdfKD+bpRaNJ3Bu3J8cG5Cq1m5CtdWY7Ba7WMTiCeztdePy1TPl0X98ZQdC0QSePTpcwZVll0gIxOb8Dj437kf3yTG899K2eRnrWrsJVRZDyUYgjPsiSAigSW3JaN3S6TQ64AmiNc3FLAC4ak09/vvDO0qWlS0XDggZY/OMesNFNZQBpJLReELq6MekE8p0+wcVCyUgjMQS+PB/78Yd//E6/uxne/L+/J5xP5qcZthynHCvrnegxmbE3p7FExCGY3F886njuHptHe6+qhMP7O5LNgvS0uMHBvHZRw7hgd19+KdnTmj+/EIIHB2axqaW2Vkgl00KftwlyJooe2za0zRlULKUZ8tw4pjvHsI6+Wd2IfxslkPPuB/RuMD6pvR7gNtrrYsuIDw25IUvHMNlq2bKXLe2VaO1xoqnDi+8gHB0OoRPPfAWNn/5GWz44tN497+9gsf2D0AIgX9/4TSMOh3+8MqOeZ9HVNpGLsoFG6WSIJdVcpXIUug0OugJojVNuSggXUh724ZGOFQ2qlqoVAWERNRARJ8novuI6MfKR7EvTkQ3EdEJIjpNRJ9Nc/+niegoER0koueJaP5PAGNMcyPToaIaygBSl1EAi65sNByL43vPncTnHjmkacZCCggzD8Ouc5gWRFOZB/f0YU+vG2sa7Og+MYaTI/nt7RrwBNGaY2gxIO0du2Slq+xDyYux88wEvKEY/vSa1bh1WwuEkEY3aCmeEPjec6ewsbkKd+1ox8/f6IVX4+ZMA54gvKEYLlgxOyCssUnvz1KUR86MnJh/lb2cw52VC1Rqu4y65J9Z9zIpGT05Ig0SX9c4P0MISE2AFlsDjQP9HgDAJSn7ZYkIN21uxqunxhGKLpyLlt5QFHfd9waePTKC37+4FX9+3WrEE8CnHtiP27//Gh7e148/uWZVxr/PHXU29E6U5vjMdMpWFxA2OMywm/ToKdF6ymnUG170GcBc1GYIHwNQDeA5AL9N+SgYEekBfB/AzQAuBPABIrpwzsPeArBdCLEVwEMAvlXMazLG1BnTIENYrQSEi6zT6H+9eg7fe+4UHt7bjw/9eJdmJa/ToVjye5JOnd2EiQXQVObxA4NY1+jArz92FUx6HX69py+vzx/MUloz16UdLpwZ8y+avXjPHh2B3aTHlWvqsLnVCbNBl+zIqJWXT47h3Lgfn7hhLW7d1oJ4QmgedConjHP3ec4EhKXIEEr7a9rSZAhXVFtg1FPJ9j6lCshNZax5zCEEls5ImFxOjXpBBKxtzJAhdNnQ7w4ikVg8WwF6xv2wGHVoqZ793rtidR0i8QQOlnh2Xz4e3NOPs+N+/L8Pbcc3/s8W3HPTRjzxl9fg8+/aiEAkjrt2tOMzN67P+Pmr6u3odwcQieVf7p+LMp+4QWVASETorLeXJfNfSpFYApP+SNEXyRc6tQGhTQjxd0KIB4UQDysfRb72ZQBOCyHOCiEiAB4AcHvqA4QQLwohlL8QbwBoA2OspCKxBCb8ETQVmyGUN18vpk37sXgCP9/Zi6vX1uGrt2/C0aFp7NboZHwqGM0aENbazQhFE8kT1koYnQ5hd88k3r21BbV2Ey5qr8HePPb4JRICg1OhtJ3Y0tkut7VfLLPN3jw7gSvX1MFi1MNs0GNbew12axwQ/uLN86h3mPHOTc24tMMFk16n+WwxJSDsqJsdEDrMBuh1BE+wBBnCyQAaq8xpu/AZ9Dq019rKkyEMyxlCtXsIlTLaZRIQnp8MYIXTkrFbYnutDZF4AiPexTOy4dy4H5119nl74pXfP1r/DBdKCIGf7ezB9g4Xrl470zBGryN89Lo1eO7T1+Obd26FMUvHzpW1NiSEdGFOa8kMYZX6ZnOd9fay/FyXkvJ1N6gslV2s1Ba8PkFE7xJCPKnha7cCSL303A/g8iyP/wiAp9LdQUQfBfBRAGhqakJ3d7dGS9SOz+dbkOti2lhKx3ciKF1Z9Az1oLt7oODnOTclnXi9vuctBM8vjtr6E5NxDE2FcMcqAZf3DKwG4F+f2I27VkWLPr5ufxiesWF0d6c/+RjrlwLn3z73MhpsldnevXs4BiGAKv95dHcPoEaE0d0fw/MvvKiq5f5UWCASS8A/2o/u7pGcj4/EBfQEPPrKAehHCu9oWwy1P7uRuMDZsQA2OyPJx7tEGC8OxvDCiy9Cp8HojIlgAs8fC+KW1Ua89srLAIBVTuC5Az24ypb7+6nWKyciMOiAE2+9gVNz1m0zCBw93Ytus7Z7qw6fC8KpR8bvdRVCONwb1Pz36Nzje6hH+jnbt2snbEZ1x8yiBw6eOItuXeG/DxeLw+eCcOgyHyf3mHTB6vHnX8eGWm1a7E8EE/jFsQh6pxO4ZbURN6zMfOFsLjU/v0fOB9BWpUv7uBYH4Zm9p7GJ+vNctfYGfAn0TgRxQ3Os4J+D8Qnp7+6TL72BC+u0HYFw4FgYZj2w6/VXVX+O8EbQPxkt6HfkQjmvOuuRvqcjvSfRHTxb4dWUjtqztE8B+DwRRQAol/uFEGJ+X2L10r0z0tYgENEfAtgO4Pp09wsh7gNwHwBs375ddHV1FbGs0uju7sZCXBfTxlI6vm+ddwMvvY5rd2xF18amgp9n1YQfX9nZjZVrNqLr0sWR3D/y4mkAJ/Cnt14Hl92ErqG9ODQwBYfDXNTxDUXjiD39NDavX42urrVpHxM/NoIfH96DdVsuwUXtNWkfU2q7nzkOve4sPviuLliMekw6+/Fs7wG0b9qO9Wm6Ds51oM8DvPgaunZsRdeF6t47Fxx9BR6dCV1d2a4Hlo7an92D/R6I372Gm67Ygq4tKwAA/ZZePNNzGBdccgVWVOfeN5nLP//uJECn8HfvuQbtcjfOl7xH8KvdfbjuuuuL6vqb6v6+Peis9+OGt83/k9q4txu2Gie6ui7R5LUUf//mC9je6UJX18Vp73/ZexT37zqP66+/XtO5lHOP78HnTwHHT+LGG65XPRutYdcLsLkyr30p+fzO53HFmjp0dV2U9v62US++s/dlNK/eiK6LWot+vZMjXvzVD3ciGgPaax349akA/vrO65PdXXPJ9fMbiycw/uzTuOOyTnR1bZx3/7XjB/Hs0WHN33eF+OnrPQCO4E9uuTr585+v1RMB/OPuF1HfsR5d29s1Xd+jw2+hyevJ629hv6UXT54r7HfkQjmvihwZBt7Yi7dftR1b2yrzt7kcVP02FEJUCSF0QgiL/P9VRQaDgJQRTH23tgEYnPsgInoHgC8AuE0IUfkNNowtcSPTSicxbUpGF9Mewj09k1jb6EiejOzorEW/O4jJUHH7MabkxjrZS0aV9vaV+zV3ZHAa6xodyXKxLa3SoONDKvfYzAzvVf+Hf0trjRRsLfDxJMrg9NRGLEozlHNj2pREPXNkGJd11s46GVzXWIVAJI7BKe1KwHonAuisS3/CWWMzaV4yGosnMDQVynqSu6rehmA0jtESj57wR2IwG3R5DcqutZkwuYhK3wsViSUwNB1K2/hH0SD/XRidLv44xRMCn35wP/REeOKT1+Jf7roYwWgcD+zOb99yNv3uIGIJgc4MY3C2tFXDHYii3135GXKvnxlHm8tacDAIAM3VFhBJc/O0Nu4Lo86RXyWHsmd4IXx/CzXq1eacaKFT/RuRiG4jon+SP96twWvvBrCOiFYRkQnAXQAen/OaFwP4IaRgcFSD12SM5aCc1KvpFJlNlTyAfWqRdBlNJAT29rqxQ56PB0gBIQCccpc+IFTa21ey0+iRwWlc2DI74DHqCadGfao+f7CA987WtmpMh2Il64ynlePDXliNeqxMOVlTTjLPabBHZswbxvFhL65b3zDr9nVy+3+1xyAXIQR6Jvzz9g8qXDYj3H5tf2aHpkKIJ0TahjIKZT2lblEfCMdVj5xQuOymZbGHcGgqCCHSjwZROC0GWIw6jEwXv4fwzXMTODwwjS/ccgFW1duxobkK65sc2HVOuz2zyu+kTO+9rW3yRa+ByjeWeeu8B5d11uZ+YBYmgw6NVebS7CH0RlR3GFUoFxf63Qv793s2Y94wiID6PIPhxUbt2IlvQiobPSp/fEq+rWBCiBiATwB4BsAxAA8KIY4Q0VeJ6Db5Yd8G4ADwayLaT0SPZ3g6xphGBj1B2Ez6ZMfBQhn0OjjMBkwHK9ckJR+nRn2YDsVwacfMH+QLVlTBZNDh3FQZMoSOyg7A9gQiGPOGcUHzTECoNPtQe5I+4AnCbtLDaVW/Z1Q5ITu4AE7Isjk/GUBHnW1W2eYKpwVmg06TIOa10+MAgGvX1c+6fa08o+9UnuM/Mhn1hhGKJjJmCKutJs0v4szMIMyWIZQDwhI3oAhE4jlnZM61UGaEltpMJ9jMx4mI0OS0aJLJfe7oKEwGHd65qTl529a2Ghzsn9KsYmBYDlybM4wM2NBcBaOeKt5pdMIXxqg3POuCXKFaaqzJC7taGveFCwgI5QzhZOkyhDvPTOB3R0dKVmUy6g2jzm7Kq6pgMVL7W/FdAC4SQiQAgIh+CmkkxLzZgfmQm9Q8Oee2e1P+/x3FPD9jLH8D7iBaaqya7KdwWgyLpmRU6TSXmiE06HVYXW/HsL+4q5vTKgJCu0kPk0FXsfb2yrDplXMChVV5DDqWRk7k995Z3yQF3Yf6PbhtW4v6Bcv29rpxoM+Dm7c0a7KPL5N08xV1OkJHnU2TcQmvnh5Hjc2ITS3Vs2532U2od5hxakSbDKESvGbKENbYjJrP3FNmEGYrhVNGT5R6ZlkgEoPdnF+GsNa2PALC/uRxyv5z1FRl0SRD+PzxEVyztn7WTMhtbdV4aG8/BjzBrIGpWsmAsDp9QGg26LGx2YmD8qzCSjkxLF3w2dhcfEDYWmPFYY0vsMXiCUwGImjIM0tmMerRUGUuWcno3l43PvCjNwAAH766E1+6dZPmrzFRQCC8GOUT7qbupKzO+CjG2KI2OKVusLgaTqtx0ZSM7u11o6HKPKskEJBmtQ35S58hJCLU2ys3nF7JDsz9+lfV29Ez4Vc1d2zQE8pr/yAAGPU6XLjCiQN5XqGPJwQ+/av9uPM/X8dXnziKG7/7Ms6OaRM0pTPoCab92lprrBgqcn+fEAKvnhrH1Wvq03ZzXV1v16ykVnmeziwlo4FIHOGYdsO6+yaD0OsIKzKclAP5Z6ML5S8gQ+iymxCMxhGMLJwB5qXQ75aOU6ZsmqLRaS46QzjuC6N3IoArV9fNul1p2qF233Iuo9NhVFkMWY/51rZqHBqYquhsxeNKQLgid/OuXFpdVgx6Qpp+PZOBCIQA6gsYvdDmsqLfU5oLPf/49HE0Oc1499YV+O/XevCGxiN6AMAdiCTHzyxlagPCfwDwFhH9RM4O7gXwjdItizFWKUqGUAtOqzGZHVvIhBDYdW4S2ztc87JbaxocGAuKok6Q1QSEgFQ2WqmmMkqGcG4Wp7PejnAskbzSnk2moCmXrW3VODIwhXgeJzD/9sIpPPLWAP7yhrX47SevgUFPuOehg3m/thq+cAxTwSha0+xDaq4uPltyZsyH4enQrNljqTrqbJqVUvZO+mHQUcZZkdXyiY+WF3L63AGsqLbkLLnqrLOXPkMYzj9DWKc0fNI4c7rQ9LkDaKnJfZyanNJ7vpgSPSWDtbl1dn5hbaNUIq3VMPPhqRCacgS4W9uq4Q3F0DtZuX1ux4enUe8waZKJaq2xIhJPYFzDvyXjXum9X8j62ly2kmQIT454sevcJD589Sr803u3oaHKjPte1n4shDsQhcte3BaaxUBtl9H7AVwB4BH540ohxAOlXBhjrPyCkTgm/JGszR/y4bQYMR1a+HsI+yaDGPAEceWaunn3rWlwICGA80WcqCon10qjnUxq7eaKlab1uQNw2YxwmGevMdlJM8cJmvLeKSS7vLWtBv5IXHWGb8wbxn90n8Gt21rwmRs3YFNLNT52/Rrs6XUXdZwyGczSPbXZacW4L1LUBYNd59wAgKvSvP8AKSgf9YYRiBT/s9QzEUCby5rxpN8l7x32aNhVs98dzLp/ULGy1ob+yUBJO84GInFYMwxdz0TpOrzUG8v0u4Noq8l9nJqcZgQicfjChb8flYBwU+vsEkm72YA6u0mzJiQj3lDOjOeWVikrWcmy0XPjfqyW9wsXS/kdrGWn0Qk5uCwsILRi0BPM64KfGo/tH4BeR3jvpW2wGPX4wI52vHhiNLlnWStuP2cIQUQb5f9eAmAFpFERfQBa5NsYY0uI0to+U/YgX06rYVFkCHeelRp6zC1fAqSSUQA4U8RogelgDA6zIeeV9zq7CeMVKxkNzCsXBVI6aeYICIt57yjjLQ4PqisT+/nOHkTjCfzVO9Ylb7tlqzQb8IlD86YXFU05sUoX7DZXSydIxbThPzTgQbXViI4MjV6U27UoG+3N0mEUAGqs0omPlgFh32RA1UWmNpcVXjkbWyqhaBzWAprKAKjY/t5yUXucGuSywbEiykYPD0yjs86WHE+Uqr3WlqxYKNaIigzhuiYHrEY93jpfuYCwJ8somHwpF64GPcXv81SM+5SAMP/AqM1lRTQuMOrVbj0A8PyxUWzvcKFODlLvvLQNQgDPHRvR7DUSCQFPMMoBIYBPy//9TpqPfyrhuhhjFTBz4qvNH6bqRVIy+vqZCdQ7zMlypVRKEDBcxD4xbyiaMzsISCeengqVpfW70zdxUNtJM5lFK6Cxy5oGOyxGHQ71T+d8bCIh8NDefly/vgFrUq6ot7ls2NhchV3nJvN+/VyyjWJplr/eYspGDw1MYUtrdcZmPMp+v94iy0aFEOgdz37iqXQX1qqxTEieLahmtpryGK2CgXQCkThseWYIa5dBhjCf46RkiYoJkI8PT2fsqNlea0vuaS5GIiEw6g2jyZk9q2XU67C1rRr7zruLfs1C+MMxjHnDWS/U5EMpbR/QcN9esmS0oD2EyugJ7TKWfZMBHB/24vcubEre1lFnx6p6O14+OabZ63hDMcQTIlklsJRlDQiFEB+V//dmIcTbUj8gdR5ljC0hM6VxGmUILUZ4wzHNS0W0JITAzjMTuHJNXdoT8lq7CQYChoo44feFY/NKMdOptZvgj8QRipa3eYUQAsNTobRNP3Q6QqeKTqPJiwkFlBsb5MYyajrj7e6ZxOBUCP/n4tZ5961tdOCsRkPiU41mmUOllKMNTRX2/gjH4jgx7J23lyqV0vm12P11k/4IvOFY9gyhHBBOaZQhVE4Cc3WuBGbGUmgRDGQSjMZhzXMOYa2tsiNhymEgx7y+VMrM1PECM4ShaBy9kwGsa0zfQGVlrTQ2IRYvrpmXJxhFLCHQqCKIubTDhaOD0xVpHKRcAMnU6ClfTosRVWaD5hlCk0GHKhV/x+aaGU6vXYD6pnzh79p1s+e2Xr++AW+cndSsKZZyYcxV5BiuxUBtU5nXVd7GGFvEBjxB6CjzzKZ8OeUmKt4FPHrizJgfo95w2nJRQOr+6bIQhgs84QfkgFBFhlApS9GyXE8NXziGYDSesbSqs96Wc/h6se+dza3VODKYu7HMYwcGYTXq8Y4Lmubdt6bBgX53QPOAetwXhsuWfg6V8vUWmiE8MexFNC6S8xjTcVqMqLObis4QKgFlZ322DKGcDdMoQ5gcOaFiD6ESNPaVcIh1MJJ/QFhtNUJHSzsgnAnc1WQIpffIeIHfjzNjPgghjZxJp91lQzwhCr7IopiQyxxrVex7u7TDhVhCYH9f+ctGlZ/rTCXjhWh1WTXNyI35wmhwmAsaR6VUVmh5oWdvrxtVFgPWzanquWJ1LYLROI4PaTO3VWkktewzhETUTESXArAS0cVEdIn80QVAu3cuY2xBGPAE0ezM3WVOLaWrZrmH0496Q/jEL/fhfT/YmfMP/M4z0v7BTA09AKDWQkWdnKjPEErfr3KfeI7I+98aM5RWrap3oG8ykPWK/YC7uPfOxSulxjLKPK50IrEEnjw0hBs3Nc2aXaZY0yg1ANJqRINCmkOV/oTAaTXAatQXfMFAGYi9JUuGEJA7jRY573DmxDNzJsJu0sOoJ3g0KvXuz9C9Np0qixEum7FkJaOxeAKReCLvpjI6HcFlMy2YLqO+cEzzix5K9kZNhjC5p9JXWIZQmam5ril9ExWlymCwyOHqSklrvYqT+e2dtdARsLMEYwtyUS7UzJ0BW4yWGmvR379U475IQfsHAWkWYb3DrGmTm329blyy0gXdnDE9SqXFQY3mMHqSGcJlHhACeCekvYJtAL6Lmf2Dnwbw+dIujTFWbgPuYEElf5k45axYOYfTxxMCn/jlW3ju2AjOTwbwvh/uxPNZNpk/tn8QqxvsWa/OFp0hDMVU7SF0aZydUWtUzm41VqXP7q2qtyEaF1lLkAY8xb13dnTWAgB2nct8QvbyyTF4AlHcflH6AfZrkg2AtJ1HKJ0MpQ+WiQjN1ZaCS4oPD0yh2mrMeSLeWWfXJENIlP2kn4hQbTVplqXudwdhMujQoLI7obR/rDQBYSgmXdDINyAEpAzBQthD+POdPbjiG8/jpu+9rGkJXt9kEEY9oSnD74BUBr0OLpsx2WgkXydHvDDIpejpKE1rim2wpcx0rVPx3qu2GrGltRqvnx4v6jULMegJwmkxpG2wU6jWGmuyDFgLE76wqu9jxvW4rMnGY8XyhqI4OerFJStd81+nxopauwmHNOoYO+mXfg8u+5JRIcRP5f2Cd8/ZQ3ibEOKRMq2RMVYmAwXOkctEKRkt53D6h/f1Y9e5SXzt9s146lPXYmNzFf7853vx6z198x57ZsyHPb1uvPfS9qylMC6LDsNThc/dUpshVMpSyp4hlLu/ZWq+oGSUeiczByQDnmBBIycUbS4bWmus2N2TubHDYwcG4bIZ5+0bUayulzIOufY75ms8x8lQk9OMkQIvGBwamMLWtswNZRQddXYMToWKygz1TvjRUm2F2ZA9IHLZjJo1N+p3S++LuVfyM2mvLc3MMgDJsR35lowCUlaskl1GEwmBr//2KL742BFsa6/GuC+Cr//2mGbP3+8O5HWc6h3mZMCVrzNjPnTU2WAypD8FVS4eFNuVUhmVUKuy3O/qtfXY3+cp+xaHQU9I07+7gJQhnApGixoNkmo8S5WEGm01Vs0yhIcHpiEEsK19flUFEWFLa3Wy8qJYHi4ZnU0I8TAR3UJE9xDRvcpHqRfHGCufWDyB4alQUSf1cylXPMvVaTQUjeOff3cS29pr8J5L2+Cym/CLP70cV6yuw98+dBDfffbErKDu20+fgNWox52Xzm9QkqrWTIjEEwUHar5QLG2J41yVyhDOlIymzw505GhqEk9ITWmKzS5ftqoWO89OpN1H6A/H8Lujw3jXlhUwZihLtZr0qLEZi8rmpjPuzX4ytKLaiuECMoSRWAInhr3Y1JK9XBSYOQbFZM96JwJZ9w8qamxG7TKEnmBec03bXTb0uwMlaUQVihSeIayzmwoukSxWPCHwqV/tx49eOYcPXdmBn/3J5fjg5Svx7NGRorrbpupzB1WV9SrqHKaCM4Rnx7LP3HPZTDDoqKixFoCUISRSn925YWMjYgmh6dgCNYamgmkbehVDq7JbQGo6NpGlSkKNlhoLBjxBTWaMKs3HMpXZX7DCiTNjvqKbEgHSxVmDjgpqprPYqAoIiegHAN4P4C8BEID3Augo4boYY2XW7w4ilhDJuXNaqJb/EJerZPQnr/dgaCqEz928MZlxqbIY8ZMP78D7t7fjX184ja/85ihi8QR+c2AQTx8ZxiduWJuxVFJRY5Gea7SAE5REQsAXian6g6J0eCx3hnB0OgyH2ZAxi9lUJY2e6M2QeRuZDiGWEEVf5e7a0IBJfwQH0pT7PLZ/EKFoAndckj14b3ZaCgrOMglG4vBH4llPhpqcFoxMh5DIM4g5N+5HLCFwwYr0zTVS5QrK1cg1g1BRbTVpdlFiwJ1f5ri9VppZplWgkyoQLTxD2FhlLmrWZDEe3NOH3xwYxN++cwO+fNsm6HWED1y2EvGEwJOHhjR5jQG3uhmEiroCM4TxhEDvRCA5381tASEAACAASURBVDUdnY5Q7zAXHxD6w6ixGlXva75kpQutNVY8tl/7WabZDE2FkuNrtNIqdwrXomx0Su7WWlTJaI0V4VhCkzm7hwam0FJtybietY0OROMCfRpkJN2BKGpspoKa6Sw2anf/XyWE+GMAbiHEVwBcCaC9dMtijJWb0kVytYYBYXIPYRmayngCEfzHi6dxw8ZGXDGnY6hBr8M379yCj1yzCj95vQe/988v4zMPHsClHS589LrVOZ/bYZT+GBSyhygQjUMIqOoyatTr4LQYyr5XacQbytqaXacjdNTZ0JshOzWYZU5fPq5f3wC9jubt+RRC4Gc7e3DBCmfafSOplOBMK0oWJNseuGanGdG4yLvpyMkRqYFOuvmXcxU7i3AqEIU7EFU1/LrGZtSkzDsUjWPcF87rfbGytvhMaCbKSIFCAsKmagu84Rj8GpXgqRWJJfCdZ0/gss5afLxrTfLEdFW9HStrbdh5pvgmKIFIDOO+SNo5pJk0OMwFZQgH3EFE4omcf2caqswYKzIjO+GL5BXE6HSEW7e14JVT42XLBoeicUz6I2jROkMozxLWIkOoBHHFlIy2urRbz+GBqaxjepS95KdHi99L7vZHlsX+QUB9QKgcwQARtQCIAlhVmiUxxirhnDy/TcsMod1kgI7Ks4fwP186A284hntu2pD2fiLCF999If7tAxejzWXFnZe24r4/ujRj+WEqp0k6CStkD5EvJJ1AOszq/qjU2k2YLPPYCakkMvuJ08razE1N8plhlk2NzYTLV9XiNweGZmXb9p134/iwF390RUfOK7VNTrOmJaPKSW9dlpMh5ep+vq97atQHHUnjMnKpsRnhtBjQU2BAqOz/VJch1CYgTL4vVMwgVCjjKUrRaTQo778spGS02PEihXrh+AjGfRH8xdvWzHvvX7G6Fm+em8w7Mz2Xsrcrrwyh3YTpUCzveW9nx6WT9Gwlo4AcEGpQMlqX596v2y9qkTKvh4eLem21lO7VKzTeQ9hQZYZBR5rs21N+BxZTMqpcFCo2Y+kNRXF23J+1K7Py3tKiuZg7EFkW+wcB9QHhE0RUA+DbAPYB6AFwf6kWxRgrv54JP6rMhrz/gGaj0xGcVmPJS0bHfWH87PVe3L6tBRubnVkfe+u2Fvz8I5fjH+7YqvrqcZUcEBZSRqds6leTIQQq081wwh9BfVX2476q3obeifR7u5QmIFo0Rnj/jnacnwzg1ZRufz/f2YsqsyFjd9FUzU4Lxn1hTfaPADPlu9neK83y1f28A8IRLzrq7LCoCFCICJ319oJHaiRnEKoMCAOROKJFfg+V94WSrVCjpcYKImhS7jVXMkNYRECoZTmyGg/tHUCT04zr0jRSumJ1HaaCUZwYKW7mWn8yIFR/nOrlioJ8y9vPyhceV+XKEGpUMprtQk46G5ursL7Jgf99a6Co11ZrSO68qXWGUK8jtNRYNbmwomlAWOTP9dHBaQDImiGsthrRUGXGGS0yhAHOEM4ihPiaEMIjhHgY0t7BjUIIbirD2BJybtyPVQ12zWvlnRZjyZvK/OjlswjH4vjLt68ryfPL4wEL2jOjBIRqN6XX2rTbv6XWhC+MOnv2P/ZrGx0IxxJpW90PeIJw2YywmYrfeH/T5mY0VJnxnWdPyPuN/PjNwSG8d3u7qsY8TdUWJASKLjdTuAO5244rDSHyDRZOjnhVlYsqOursBWcIz475QDRTkplNtUbdgQvJPJkMOrRUW5PzC7WkZAhtBZaMAuXNEEZiCbx2ehzv3NQMfZrun1vbpJPiI/JJcqH63MqsyPwyhAAw7s0zIBz3wWnJfeGxoUoqSS2muZAnEM17fhwR4c5L2rC3161JyWEuQ57SZAgBKejWouPyzPiOwi8WO63SHvViM4SH5IYy2QJCAFjb4NAoQxhV3aV2sVPbVOYAEX2eiNYIIcJCCG36uTLGFowzoz5N9w8qnFZDSUtGx31h/GxnL27b1qKq9K4Qeh2hxmYsqNmLUjKqJpgByp8hjMUTcAeiOf/Yr22UGp8oQ6VTDRY5gzCV2aDH399yAQ70T+FzjxzEFx49DIOO8LHrc+/1BFIyORqVjSptx2uynFjWO8zQ6/KbVRmJJdAzEcD6DMO50+mss0l7sGL5Z+5OjfrQ7rKp2j+nVUDY7w7AoCM0Zehem0mbS5vMxlwBOUOoJiM7V1OyZLR8jWXeOu9GMBrHNWvr097fWWeH2aDD8aHiAsJ+dxDmPGZFAjMZ83F/ft8P6cKjI+eFx1q7CQlReIdqIQQ8wWiyUVc+7rikDQYdpR1VpDUlQ6h1l1EAWN0gBYTFdvYc94Who+KGsxMRWmusRY+UOTwwhWanJTmrMpM1jXacGSvuaxdCwBOIZP3dv5SoLRm9DUAMwINEtJuI/oaIVpZwXYyxMpoKRjE4FcKGHOWWhai2GjEdKl0jBiU7+IkbSpMdVEh7+wrJEEonNGrmEBbzOoVSXitX+aySyTqV5qp5vp0kc7ltWwv+omsNHtzTj9fOjONrt2/OOBJjLq1P3N2BCPQ6SjZISkevIzQ4zHllCM+N+xFPCKxvyt1hVNFZZ0dCAOezzIPM5PSID+tUZiM1yxB6gmiutqTNbmXTXmtLZq20FCoiQ6h04dV6pEk2r50eh15HuGJNXdr7DXodNjRX4dhwkRnCSanDaD7VIUrwmG/VxNkxP9aouPDokssyCq2W8IZjiCcEaqz5n8w3VJlx3foGPHFwSJMxCdkMToXgshkLukiRy+oGBwKReNG/C8d9EdTaTXn/HM/V6rIW3VTm5IgPG1V0ZV7T4MBUMFrU7FBfOIZoXKCWA8IZQoheIcS3hBCXAvgggK0AzpV0ZYyxsjkl70HZ0Kx9hq2UJaMTKdnBfErvClFrM2GygJJRrxwMV6ndQ2gzIRRNJPc7lZpyQlefoyym2mpEk9OMU6Oz9yslEgJ97kBe+49yISL83U0b8co9b8OLn+nC+3aob2qtZDq1Gt3hDkRRYzXmPFlurs6vu2k+HUYVG5qlE6ETw/mVQsXiCZwb92OtymykU7MMYX4zCBXtLhtGpsPJAE4rxXQZBaRjrGR0ymF3jxsXrnAm57mms7G5CseGvEUFLtJxyu/nV/k5y6fTaCASw9BUKOvICUVNciZrYe/BKfnzCskQAsC7tqzAgCeIAxoNOM9kyBPECo1HTiiUip+zRZZOSkPpC98/qFBmERYqkRA4N+7H6vrcv8eUaqFi9hF6inwPLTZqM4Qgok4iugfAAwA2ArinZKtijJXV8WHp5DSfbIVaTos2HQvTue+V8mQHATlzV0jJqNJURm3JqDKLsExZwpn9Ibn/4K9vqsLxodkBYZ87gFA0kVfpo1rttba8u94q+z0m8yxly0QqGcp9QtDstCQ7BqqRT4dRxdpGB/Q6wvE8M0LnJ/9/9t47vK3svNd9FxoBEGDvpEQ1ShpJ0zVF08RpthPHHseJaxw7xXGSm+aTOCc+N+W0FJ+TxOceO3Fuxnau7cQ9cZnYPp7OGU/TjGYkjXoXe28gelv3j40NqoDE3hsbrOt9Hj4kgQ3sRe6C9a3v+36/KMlMlq4mY9e3niEsdSFHyxybXyjYWK9NkEstL7uafMmoy1pAuLHOT9+UuTFNhhN849W+vJm2UTJZyZGBGW7ZWLPodjtaqpiKJEvKhPRORkz1D4JWAu9zO03ZM+j9bJsNTOj18sQZi/fB+cm8tezOw7uaC1rg2M3wbJy2GvvLRYF84F1qL91k2Lw4TyHaa/zMxlL5z0SzjM7FiaUyhhYUtjbpSqPWeyj1z/tSSmVXE0Z7CA8A3wGcwHuklLdLKf+2rCNTKBRLxumROSo9TlvL/nSq/eVRGY0lM/zLy738zA3lzw5CCSWjFnoIwZrnoRUmI8VtFXRu6Kjm9OjcFZmb02VcTLBChctJoMJliwEywHTEmDBFS7WXUTMB4egcG+v8pkrFvG4nmxsqOTlsTlVyfsFn6UpGk+kso3NxyxlCwPay0Xgqg9ftwGGx9G1jnZ/+qajhbFwkkeaDnz/AJ79zlHf/w0scuGDcM/DM6BzRZIabi/hu6pPjSxbFQ2aiSULxtCH12aupD3hMXWe6wqiRCb2+MGY1Qzid7/21lt2p9rm5vr3aFp/HxRgqY4awpcpL0OvKX/9WmQgnbckQ6n3mVpVGdWssI1oHrVVefG5nScJA+jmkbCeu5CNSyluklH8lpbxg186FEG8TQpwWQpwTQnyywPMVQohv5p4/IITYZNe+FQrFPEcHZ9ndVm27wiho5vTxVNa0X1Uxnjw5SiSZ4QO3L007c11O7MVsaVY4mcbjcuBxGbvdzme4liYgzJsOF1EZBbixo4ZMVnJ8aD7boZc+dq2QgBCsZ3MLMW1QVKAlZ1xudPX73FjY0v9sZ0vQdIbw6OAsLofIl5wWIx8QluCHOTwbQ0osiQ3pr9EVGO0imsxYspzQ2VDnJ5xIGw5SPv+TC5weneN/v/8m2qq9/Mn3jhn2DDzUNwPAzUUyhPmyQIsBoRk7kqupN2lOr2cIjexL7/2znCGMFVcHLsZdW+s53D9DxGJGqxiRRJpQPE1rmTKEQgiua63iZImiQ0ZUqI2gLzhb7SM8r2eYDSwoOByaTY9VVWa4LCBcJyWjRjXCp4UQXwTapJQ/JYTYBeyTUn7R6o6FEE7g74GHgQHgNSHEY1LKE5dt9qvAtJRymxDi/cD/AN5ndZ8KxUoimc4yHU0yEU5oJT9hrexnKpJgOppiNppiOppkLp4mlcmSlZJ0ViIlVLgceN1O6is9dNZXctPGGvZtqS+qvFWIRDrDiaEQv3T3Jvv/SOb7kUKxNI1B+xrnHzs8REuVlzs219n2notR7XOTzkqiyYzhbB9oGUKjlhMwX56yVNYTk+EELoegyld8jDdt0Canh/tnubVT+7+fGQ3TXuMzXBK7FNgZEM5EU1zfbqxkFDR102IZ61Sup+/hXc2mx3NDRzU/eHOYsVDcsNDOscFZtjcHqTBYKulxOfC5nSVlCPXy2TYL2Y+moCZEU6oAxdXEUqUFhLplR+9kpKgUfTyV4Z9f7uXBnU08clM7Qgh+9+uH+PHxEX76+tai+zrUN01dpaeoTUh7jQ+3U1i2F+jNTZg7682X9jYGPAyZCNovjGv3CiM9nEGvC4eYL/00y2zu/lltQVRG566tDXyu5zyvXpri/h1Nlt9nIfKm9GVQGNXZ1VrFtw72k81KS5nxWDJDJJkp6lNrBL1aYMDidX1xPILP7czfa4uxqd6fr2CxwnREO/fWi+2E0U/wLwH/H/DHud/PAN8ELAeEwO3AOT3jKIT4BvAIcHlA+AjwX3I//yvwd0IIIcst+2Qzp0ZCfPZQnK/3H8w/VugvWOiPKvzXXvug0fcs9O8rvJ3191uIwu9p8G8p5bULDsie95ydjfHZky8V/F9kJSTSWeKpDLFkhmgyTTyVJbmA6bMrZ3FQ7XNT6/fQEPDgcjpwOQROh0AIQTKdIZrMMDgT48XzE/zTixdxCOje0cRH79nMXQvIlBfi1PAcyUyWGzsWX4m2ip5tmIkmLQWshUims7x0foKfu6XDcvmXWS4vozMVECbShk3pYekzhFMRTUHOSHa4qcpLe42PVy5M8qv3bEZKycFLU1zfsbgn1FJTX+lhyAY1SCklU9GkoZKheXXT4gFh72SEdFZaKnW+c4umOPnyhUkeuam96PZSSo4OzvLWXS2m9lPtK633VxfYaa4yf807HYKWqtIEKAoRS2UsC8rAfNDUNxUtWsr57KkxJiPJ/ELb269v5a8fP8WXXrxkLCDsn+HmDTVFr0uX08HGOn++nM4slyaiCKFlP81SX1nBmyZEVy5ORIoa0us4HIKaEjxZ9Syuft+2wq2dtXicDl4+P1mmgFC3nChPySjAda1BoskMfVNR0/3YcJkpvQ0ZwsZABW6nsF4yOhFmc4Nxr+RNDZU8dXKUdCaLy2lYMiXPdDSJQ7CoqNNawugspUFK+S0hxH8CkFKmhRCl1n+1A5ebvAwAdyy0TW6fs0A9MHH5RkKIjwEfA2hubqanp6fEodnLhZkMw3NpRiNjVzxupjyv0JZGX17wtQY3NDPVtn2MJby24HYLbFjK6/WHnGSIhxf+YPQ6BdVu8HgFHgdUOJ1UuJwE3YKqCkHQI6jyaN/9rsvPjWTua2EyWS+9c1kOjWb4ycVxPnhqjDtanHxoVwVBT/G/7sle7YMzNniSnqnTRbc3y+Ckdqt46oVXGay3J0N4akoLiGsTI/T0TBR/QYmEw2H6R84A8PRPXmZD0PiHy6XBOKSk4ftSVkoEcOjEWTanei2M1hyne+NUYHx8O6tS9Jwa5fGnnmUsJhmajfPWDdkVdd9NziUYnsoYGlM4HF5wu0RakkxnmRrpp6dncXGJ0Yi2wPPsgUOkBhafQBwc0UrQQv2n6QmdKzrGy8lKic8F33nhGNUzZ4tuPxrJMhNNUREdNXWMXNkE5/uH6emZNjU+nZcuaveVM28eZNBtftGmUiQ4cWmYnp4ZS/vXufz4DgzHSSeMn+tXk8hoC349B48X/d9/+UicoBuS/cfoGdT+/n2Nab51eop//vdnFr2HRFKSc2NRbqhOGBprlYhzrDdq6e969USC2grBKy/+xPRrI1NJJsMpnnn2WRxFPrCllJwZiXJXm8vwOD2kONM7SE/Pwn18C12/x84k8DrhpReeN7SvhdhcBY8fvsRdfvvFZZ4f0K6RvlNHiPeZD1iMEJvVPn+/8cTL3Nlmvorj/Iz2+qGLp+mJnC95PDUeOHT6Ej2+kaLbXn1sj/dH2VTlMHz+JCZSpDKS7zzeQ5Pf/P/3+NkElS54/vnnTL92NWL07IgIIerJJUWEEHcCpWrxFrp7XJ1eMbINUspHgUcB9u7dK7u7u0scmr10A1tqelhp41LYR0/Pyji+8VSGf3zuAn//7DlGj8CXfvm2oquCX/nSa2ysC/Nzb+suSw9h68gc//O15+nYdh3dN7bZ8p4HHz+N03Gejz6yf0lW73p6erhr5x7+/vABunbfmM/SGOFzp1+mJQDd3fsMv6b2hScJNrTQ3X29leGa4n+feJFN1S66u69ejyuMZ8MET3/+APGG7YyPR4Cz/OY77zVcvrgUvBI7xSsjF9i/f3/Rc3qxa3dwJgZPPcOte3bSXaRXNZ7K8Ec/+TG1bZvp7t626LZHnz4LnOG9P7Ufv8f8JO3e/oMcGZjh3vv2F/UG++qBXuAYH37bPlMZybZTLyOEufP2cl4In8B7oZeffsjafeV7I4c42Dtd8n318uP7j2deoaJSWv6bADrfeJaEr4ru7lsX3CaRzvA7zz7FT9+4gQcfuCH/+I23Jfn+Xz3NqXQjv7jItf38mXHgVX5u/y3cbaDa46XoSb700iXuu2+/6YqJz558iR1tDrq77zT1OoALrov84MIJbr797qJZ9LG5OLHHn+aeG7fTffdmQ+/ffvIl3K7Fx7bQ9fvY2GHqZ6dKPn+OpM/y/zx9hpvvuLukbGMhDj91Bo6d5ZG37Ddczm2WdCbL/3z9CaKVLXR37zH/+hOj8MpB7t+3lxs3lF5FtO3MKyTSGbq77y667eXHNpnOMvH4/+H9d26mu3uHoX35L07xT8depnnb9ezf3mh6rN8efIPGeGhFzO2WAqMh8+8DjwFbhRAvAl8BfqfEfQ8Al5tLdQBDC20jhHAB1cBUiftVKNYsXreT33uoi69/7A5mYyl+7h9eukIA5GriqQwvnZ/ggZ1NZQkGARos+FUV41B/cX8uu7GqvBiOpw17EOrUV3pMGz5bZTKcpN5Ej8Ttm+roagrwiW8f4R96zvHQdc0rKhgE7f+XykjmShSDmDYhO+51O6n2uQ0Zl58d03qprASDAO+6uZ3RUIKfnB0vuu1L5ydprqpgqwEhhsupKrVkdC5BS5XX8n2lrcbHyGycjEERFiNEUxm8JZSMgtaTdWJocZGOl85PMpdI87Y9V5bp1lZ6eMeNbXz30OCiysuH+mYQQusXNcLmhkqS6SxDFjwSeycjbGqw5iGqKxNPGrB4mVcYNb4oUet3l+RDaId/3E0ba5CSRT9HrTI8E6chUFG2YBC0kuKbN9bw2iVrmf58yahN7R6aOb35cv6+qShZaUxQRkc/r60q8E5Hk+vGlB6MG9O/AewH7gJ+HdgtpXyzxH2/BnQJITYLITzA+9GCzst5DPhI7uefB55Zbf2DCsVycGtnHf/2m3dR4XLwwc8f4Eh/4bKrZ06NEU9l6d5hfvXMKLV+D06HsC3AkVJybDDEnval7VuzHBAm0qZ6DkGbaBmZZNmB5jFl/MPe5XTw6ffeREetn91t1fzVu8ufxTRLvg+zxHNOF7QwqjLXWm3Mi/DsWJiuEnwbH7yuiVq/m395ZfGS4mgyzQtnJ7h7a4PpwKza5y7Jh3DUhOhNIdpqfKSzkvE5+66DeDKDz11aad6u1iouTUYXVZN9/NgIgQoXd227tpLgw/s6iSYzfOf1gQVff6h/mu1NQYIGF7z0vjyzwjJz8RQT4SQb68z3loHWEwYYsp7Qx2bEMkCnxu+xrDIaiqdsyejtbqsCKLoIYIWh2VjZPAgvZ29nHadGQpYWePTrz8yi4WK01/gYnYubVh2/kPNSNOJhqdMYqKDS47QsuDQVMaYwvVZY9M4ohHi3/gW8E9gBbAfekXvMMlLKNPDbwOPASeBbUsrjQoj/JoR4Z26zLwL1QohzaFnKa6wpFApFYbY0Bvjmr++jyufiQ184kCtDmieTlfzdM+fY3FDJPSZEaMzicAjqKj22ZQgHpmPMxlLsaa+y5f2MUmXRrDuSSJtW4GwIVNjmo7cYuoKcWdPh6zuqefYT3Xzvt+62TSjITurymYvS/odmfaiaq7x5MZWFyGQlF8bDdJXgnVnhcvLLd2/mqZNjHM4t9qQyWcbm4gzOxPJiFd94tZ/ZWIoP3GHemqVUUZmxUDwvtGMFXaLeTmGZWCpjOSursysXIJxaQMo/k5U8cWKUB3Y2Fcz83NBRw40d1Xzlld6CFhRSSg71zeQVfY2wxWJA2Ju3nLCaIdQDQiMZwjAVLocpr9san9uyqMxsLGVLBUlDoIKWKi/HyxAQjszGDStmlsI9XQ1IyTVzACMMh+LUVXpM+aUuxqYGP1JC/5S561o/t42KEoGmxdBZb916YiaaWjeWE1C8h/Adizwn0czqLSOl/BHwo6se+7PLfo4D7yllHwrFemZDnZ9v/fo+PvSFA3z4n17l1s5abu2spbXayzOnxjgxHOJ/v/8mSwpcZqi3MSA8OqiV7ly/xBnCYIULIcxnCOdMqoyCHhCWP0OoZyHtUJBbSeh/z2SJ/8MZk+bWrdVeThTx/BqYjpJIZ+lqKs238Zfv3sRXD/Ty0S+/xo0dNbx4foJ4al65+LrWKs6PhblzSx23bTJvzVLtcxNJZkhlsrhN3h+klIyGEjxUwmKB7kU4NBPj1s7FFT2NEk1mSp7Y6oq6r/dOs7fA//W1S1NMRZK8dffCqq6/cs9mfu8bh3ny5Og1250dCzMbS3FLp/GAsDGoZUIumFQa7ZvSAsJOCx6EMN8OYKT648K4pjBqpsexttJDPKUpdJs9bqFY2pCVjhF2t1VxbNDeklEpJcOzcUM9oqVyy8Zaav1unj45yjtM9vGP2hy06ufapYmIqZ7mixMRGgIe01nfzQ2VRe/JhdAVpteL5QQUCQillL+8VANRKBTlobXaxw9/916+8vIlHjsyxJdevEQyk6Xa5+a/vnO3Ien6UmkMVjBuU8br9MgcQsD2JTZCdzgEVV5zWZNEOkMynTXlQwjaRGsunrY0ETKDPpEzmyFc6egZwlKtO/T+pRqDXmbNVV4mwgmS6SweV+Eg6uyoVvq0tYQMIUDQ6+arH72T//SdNzk9Osd7926gqzlIhdPBdDTJs6fHeOueFv78EfNCEgDVucl0KJYyVVIMEIqniaUyJWUIdW82O70I4yX6EILmkbi1sZKXL0zy6/u3XvP8j4+N4HE5Fi3Df/v1rfztE2f49BNnuH9H0xXnip7FuafLeBm/EILNjZWmM4SXSvAgBK2k0yEMZggnIlzXau6erS/EzERTtFSbDAjj9mQIAXa2BnnuzLilxZGFCMXShBNpUxlTqzgdggd2NvPkiRHTnynDs3FabPRJ3KwHhCazdvqCglk2Nfh5/PiIaeuJWEr77F5PJaOGZilCiGbgL7HRmF6hUCwdXreTj923lY/dt5V0JstsTOuvKHdmUKchUGG5jv9qeicjtFX7yhooLUSVz2WqZDSS0PokzJaM6hPwqUiStjJOGPQModkJ/0pH73exo2Q0UOFaMLi7mg11WjnUwHR0QfGMc7leGCsehFezrSnAt3/jroLPFQpWzFDtn++ZNXt+jOXKZpsseBDqBL1uqrwu20pGpZREk2n8JYrKAOzbWs933xi8JkCQUvL48RHu62pctG/Y5XTwZz+zi49+5SCffeYsf/CWedXE586Ms7Wx0nSgsLkhsGCv+EKcGw3TUuU13eOs4zTYDpBMZ+mbivJ2A/6Ll6OLOU1Hk6aCklQmSzSZyZf5l8rmhgDprGRgOmYpKClE/7SWndXN2svNu29p59/eGODx4yOmFoFHQ3Fu2mifR3GNX7uu9XJlo1yYiPDgTvNekJ31lfljZ8aHUV9MrKtcPyWjRmeDX0Lr9dNzzWeAj5djQAqFory4nA7qAxVLFgyClvGaCCewQxPq4mTUtg9ls5jtqwrHNeGJgMmV6oaAXvJY3j5CvU/RLsGAlYLX7cTvcZacIZwxqVRoRNzj7GiY5qoK2yXs7caqiBLAaEgLEErJEIImLGNXhjCZyZKVlGRMr3PPtgYiyQyvXrxS9PzNgVmGZ+PXqIsW4qFdzfzcLR18rud8PpCbCCc4cGHKkgn65obKXDmycbGOM2NzJYkbgZYx1Y/3QvRNRclkpen7tn7tme0jnMvdUT/ctwAAIABJREFUd+26xuav67At7wdaLzxoi0hLwb4t9Wyo8/HN1/qLb5wjnsowGUnaWjIqhGBTg7m+vlA8xUQ4YUphVCd/7ExmJHVBsfWUITQ6I2yQUn4LyEJeEKZUY3qFQrFOaAhUEE9liSRLv21cmrAuk14qZgPCuYS2baDC3CS0vgxWHYVYqyWjoCmNll4ymjRkOaFjJCA8NzZXcv/gUlBaQKhlCEsNCNtrfAxakKgvRDyp9VeWWjIK0L2jiUqPk8cOX+mU9d1DgzgdgoeuMxbQ/dk7dtFS5eW3v/4GM9EkXzvQRzKT5f1FPC8LsaWhkqyE/iljmZdsVnJuLFxy6X1Ltbeo1UpeYdTkhF6/9mZMWk/o56xdPYS6aI/ZHs3FGFjiDKHDIXjvrRt46fwkvQaDo7FcoG9nySjAJpNCLxfHzQvKXL4vMG89oS9CrKceQqMBYTmM6RUKxTohr0ZXooT8dCTJbCyVv8kvNVYzhGZLsnQ59/EyB4RTkQQ+t7Nk5cWVSH2lx4aSUXMZwtpcOdRCkx0ptUm4HeWi5aakgHBODwhLK0W2M0MYTWnXoh0ZQq/byVv3tPCjY8N5P8H+qShfO9DHz93SbjirUO1z85kP3MzobIKf/dxLfPaZszx0XZOl82OzyaBlYDpGPJUtSe0WjCnr6pYBZjwI4cqSUTPoZf129RDWVnqo9bu5YFPbA2j//0CFa0krBX5+bwcOAV890Gdo+5HccbVbCXVTvZ/B6RjJdLb4xswvKJj1UgWtOilQYb5EdSrvQbuyKznsZDmN6RUKxTqhwYSB8WLoZR/LGxAaNzvXvcqM+onp6FYOdnqwFWIynFyT2UHQFiGmSjzfZkxmCIUQbG5YWNxjaDZOJJkpWVBmKajOCelY8SIcCyUIel0lLzS01fiYjaUW9fwzSixXnWBHhhDgV+7ezFw8zf/bc55UJssffPsIbqfgPzy83dT73NpZyz/+4q1UVji5f0cTn37fTZbGs8mk9cSZ0TkAukrNEFZ5mYwkFy1VvTBuTSHyclEZM+hBul09hKAF3BdtzhB21PpM+4OWQmu1j5+5oY2vvHypaBAP8yq0dmcxO+tz2expY0HahYkIDmGtvFYrUfWb1jCY96Bdm5+PhVhOY3qFQrFO0HvixudKy9jofRcbLarilUpVzqzbaC+kPpE1KyrjdTup9rkNfWiXwkQkueYEZXTqKj0lG9NPR5KmV4i3NAY4P1Z48nF0QOsV29O2tB6aVii1ZLTUclEgb9o9bEOWMKoHhDZkCAH2tFfz7pvb+YfnzvOOz77Aqxen+O/v2kNrtfnJ8/07m/jB79zLox/eazmrVe1z0xDwGJ74Hh8KIQTsaCm1ZFS7f4wt0kd4fjzMFhOG4jpetxOv22HanD6UW7SzK0MImrCMXcJooAVbHbVL/zn2ibfsICvht776BvHU4i0clyYiOB3C9j5HveXDaOnqhfEwHbX+gr6eRrDiRahnCFd6r7edGFaVkFKmpZTHpZTHpJTW3WoVCsW6Q894ldoTp5ePtdrc02CUap+bZCZ7hd/bYujiBlUmfQhBK7crd0A4GU7QsEZ7JOorPUxEkpaFjNKZLKF42rSowK7WKkZC8YLn+qG+GTxOR97cfCXjcTnwuZ0lBISlLzTomQk7lEb1ya9dGUKAv3z39XnlzE+/90befUuHbe9thc0NlYbLGo8PzbK5odL0YtXV6IH/QveqbFZycjhk2nJCp9bvydu/GEU/Z+2czG9prGQkFCdiQ7Y6ncly0aQPn11srPfzt++5kYO90/z146cX3fbSZISOWp9tVhs68319xjKEFycipvtPL2dzfSUD0zFSGWOf26BVhyylEvtKYP38pQqFYtnQG7NLDQgHp2NUeV2mSzDtQl9xNjpJzmcILQWEXkaKqPeVylouGa2r9JBMZ/OZIbPox9hshnBPu2ZcXsjI+lD/DLvaqiyvdC81ZntmdUZDCZqDdmQIdXP60hdGYil7M4SgZbD+7oO38OOP37fswSCwaLny1RwfCrGnrbrkfeqCIyMLBIS9U1EiyQy7Le6rxu8xnyGM2ysqA8YEo4zSOxUllZEl929a5R03tvHhfZ188YWL+dLhQlyajOSN5O2krtJDsGLhXuvLkVJyccKaB6HOpoZKMllpWHAJYCqaWlf9g6ACQoVCsQS4nQ5q/G5bMoTty1Bmo2O2jC4cT+MQ1rISzVXevJ9bOZBSMhlJrNmS0drK0szpdSGLWpMZ1N3tWvbv6oAwkc7w5sAMN22wz9Or3FgJCLNZydhcnCYbSkabgl6cDmGLsEzU5h7ClcjmhgDjcwnm4osfs+lIksGZGLttyFS3VmlB+/ACQfvxIe06sJoVr/W7zfcQxlK4HMLWY21nQHh2VBPZKdXyoxQ+/tB2vG4H//TCxYLPSym5NBFlcxnaM4QQbG6sNCSANBKKE01m8kqvVticK1E1UzY6E02uK8sJMBgQCo0PCSH+LPf7RiHE7eUdmkKhWEs0BCpK9tUbnInRXrM85aIwHxCGiky4dMKJNIEKlyXhgOaqCsbmEmSzpXs3FiIUT5PKyDXnQahTX3JAaM2HqsrrZnNDJYf7rwwIXzo/STyVZf/2RkvjWQ6sBITT0SSpjLSlZNTpELRUee0tGbUxQ7jS0IOWYqV4h3O+h9d3lJ4hrPa7qfa56Z0qPNk+PhTC5RCWg58av9u8ymg8RZXPbatgi17maEdAeG5My8ptNam6aid1lR7efUsH3z00WPAaHw8nCCfSZckQAmxrDHBurLiv43zwbL3XtdNkiSponxvryXICjGcIPwfsAz6Q+30O+PuyjEihUKxJdHP6UhiaieXLyJaDfIbQ4Ir1XDxtuby1ucpLJitLtk5YiMncsWhQGcKCTOdeV2dhlfiurfW8fH7iCuXFJ0+M4vc42be13tJ4loMqk6q6MG9Kb5dUveZFaF+G0L+GA0K9z+rc+MJlgAAHe6dwOQQ3b6i1Zb+d9f4FZf1PDIXoag5aLpOu8XtML0qEYmnbxUB8Hidt1d68hUYpnBqZo73GZ9qOyG7ef9sGEuksP3hz6Jrnjg+GAOuZ3WJsaw4wEooXXVw9mwsaSymvrTdRoqozY9JyaC1gNCC8Q0r5W0AcQEo5Dayv0FmhUJSE1hNnvQRyLp4iFE+vjIDQcA9hyrJoQ1OuB6uY6bNVdEuLtdpDWGqGcCafITQ/KXjwuiYiyQyvXpwCIJpM86Ojw9y/swnvKipZrM6p6ppB9yC0o2QUNKVRO0pG7badWIlsbqjE43JwYii06HavXZpmd1uVbdnSzvrKBQPC40OhkkpTa3xayagZcajZWMqSkFcxtjQGbPEifKN3mps2Ln/p+PXt1exoDvKvrw9c89zh/hkcQtumHGzLZUfPF8kSnhubo9bvLqm1QbOeMN5fC9rnxnqynADjAWFKCOFk3pi+ETAu16NQKNY9LdVeRmetl0DqKnbLpTAKVgLCtCVBGbhcYdGcoa5RxnIBoR32ACuRumXqIQS4a2sDPrczP9H6t9cHmImm+KW7Nlkay3JhpWRU73u1o2QUNGGZkdk4mRJLp3VRmdUUkJvF7XRwXWsVRwsIGunEUxmO9M9wa2edbfvtrPMzOHOtiuNYTm13V6v1gLDW7yGdlaa8KPWSUbvZkut7s6pcDFqVy9BsnL2d9mRnS0EIwc/f2sGhvpl8GavOkYEZupqCZcti6iWgZ4sEhGdHw3Q1lWaNApqwjNEMYTSZJpbKrNnF0oUwGhB+Bvgu0CSE+AvgBeAvyzYqhUKx5mir9pHMZJky2Q+io5eiNdmgXmiVYC64MzpJnounLWcIde+nPhPKaGbQA2w71CBXIoEKF26nsHy+TUdTuJ2CSgtZFK/byUfu2sRjR4b49yND/K+nznJrZ+2KmASaodrnJpxIkzYh165fp7rVTKm01fhIZ2U+o22VaFITeKpwrW0tvT1tVRwfDC248PZ67zSJdJa7t9lXutxZ7yeTlQxOX5nJPZ7LVJaSIay2YE4fiqVs9SDU2doYIJxIl3Quvt47DcCtK+Re8K6b23E6BN8+OJ8lzGQlh/tnuMGGHtOF2FDrw+N0LJohlFJydizMNhvEd7Y0aNYTxbwXgbzWQUPl2mynWAijxvRfBf4j8FfAMPAuKeW3yzkwhUKxttDlyRdSoyvG2Jy9mQcruJwOAhUuUyqjVjOE1T5NrKFcAeH4XAKPy2GrNPtKQghRkjm9rjJnVZjiN/dvZVN9Jb/z9UOE42n+8mevt1XkYimozp0bobjx7MxoKE5dpcc2a432Gnu8CKPJDH6PNYGn1cT17dXMJdL0LnDf+MnZCdxOwZ1b7AsIN+XEbC5MXDm5PzIwgxCl9aHpZXumAsJ4uiz3Nb1H87wBdcyFePHcBIEKF9eVkDW1k8ZgBW/Z1cy3Dvbng6VXL04xE01x/86msu3X5XSwpbFy0QxhKKktvtphz7GtKYCUxkSB9L59lSEsgBDiTmBQSvn3Usq/AwaEEHeUd2gKhWItoZd6Ds9am9jlM4TLXOJopq9qLpEmWELJzWJiDaUyNpegKVixpifItX6PZVGe6WiyJB+qar+b7/3W3fzNe27kxx+/lx0tpZc9LTV6dsZM2ehoKE6TTdlBgPZa3YuwtIAwlsysaYVRnb2btMzTqxcnCz7//JlxbtlYa2sp4PZc+d/J4SvLDg9emmZnS1VJvrF6D68ZpdFQrFwlo1pgcnXga5RsVvLMqTH2b2+03ey9FH7xzk6mo6l8ifsPjw7hdTvo3lFeReStTYsrjQ6FtcoEO0pGdUVXI8qmuuDaWrVkWgijZ+Q/AJf/FyO5xxQKhcIQrdU5vyqLIimjoTiBCpflEky7MNNXFY6n82WmVthQ5zdlpmuG0VB8zfYP6tQHPKYl63Wmo6mSfaiqfW5+/taO/ERytWG2ZxZypvQ2nlf6QlKpAaGWIVz7AeHWxgCNwQpeOn9tQDgwHeXEcIgHr7M381Ptc9Ne4+Pk8LyYTTqT5Y2+aW7bVFpppL4oM2PwHIynMiTS2bKUjLZWefG6HYb88wpxbGiWsbkED5Qx82aFfVvruX1THX/7xGm+f3iQb702wE/vacXvKe9nbVdTgP7p6IJlnIN6QGhHyWhjJULAeQMqsfkMobKdKIiQl3XRSimzwNqsM1IoFGWhvtKD2yksB4RjoQRNy1guqmM0IExnssRSGQIV1icmG+v8DEzHShbUKISeIVzL1FVWlGQ7YcVyYi1hLSCM22Y5ARD0uqnyumwpGV3LCqM6Qgju2lrPS+cnr+kjfOL4KAAP72qxfb/XtVZxamQ+Q3hsKEQ0mWHvptLEa6p9esmosetYtzEoR4bQ4RBsbggYCioK8d1Dg3icDtsD8lIRQvDf3rWbrITf+8Zh6gMe/uRndpV9v3oZ50L/z6FIlqDXZcvnlNftpKPWZzBDqEpGF+OCEOJ3hRDu3NfvARfKOTCFQrG2cDgELdXeEkpG4ytCAKXa5za0Wh1JaKueVnsIQQsI01lp+X+2GOshQ1jnd5ekMlq3ziYEV6MHhEYn4+lMlolwwvY+37YaX+klo6n0svu+LRX372hifC7BK5eVjUop+fbrA+xsCeYN7O1kV2uQC+PhfLbn/xwbxuUQ3LutoaT3rTEpKhPK+WaWw3YC5pVGzZJIZ/j+4SEe3tVccuVBOdjZUsUzf7Cff/qlvTz+H+5bElN2vdT49Ehh38yBuSxdTQHb2hq2NQYM9X9OhhP4Pc6yZ0hXGkYDwt8A7gIGgQHgDuBj5RqUQqFYm7RW+ayXjM7Fl1VQRsdohnAuoW1TSg/hxjIpjUaTaebi6RWRcS0ndZUVzMZS18jhFyOblUxHU+s+Q1hjUtBjMpIkK+3v89XM6Uvz41wvJaMAb9vTQpXXxVdf6cs/9uNjI5wcDvFr924pyz5v7qwlK+HAxSmyWckPjgxzb1eDJduWy3HnhLyMln7rGUK7jel1tjZUMjAdJZEurlZ5OV8/0MdUJMkHbt9YlnHZQX2gggd2Npel3LYQWxoq8bmdBW1SMllJbyjLDR32+TVubQxwYTxctOJmMpJcd9lBMK4yOialfL+UsklK2Syl/KCUcszqToUQdUKIJ4UQZ3PfrykyF0LcJIR4WQhxXAjxphDifVb3p1AoVgatNdYyhFJK23uTrFLjNxYQ6r5ZpWYIAdv7CHV5eF3Bca1SV2lesh60SWUmK5dklXwlU+v3IASGhXnyViY2X6e2ZAjXSckoaOVxH7qzkx8eHebPf3CCf36ll09+5yg7W4I8clNbWfa5b0s9PreTp0+O8sOjwwzOxPjZWzpsee9qn5tZg9ewfm8uR8koaMIyWYkpsa9wIs1nnznHnVvqbLX7WO24nA52tVVxrEBAeH48TCKDrdYX25oCJNLZoveSiXCC+nVmOQEG+wCFEF7gV4HdQP5OL6X8FYv7/STwtJTyU0KIT+Z+/6OrtokCH5ZSnhVCtAGvCyEel1LOWNynQqFYZi43p3c4jJeBzMZSJNPZZVcYBW2ikUxniacyi5pch3NS/aWI4LRWe3E5hO0ZwoFcQNhR67f1fVcadbkP9alI0pQvnh4ArfeA0OkQ1PjcTEWM+a7pSsDlKBmdjaUIJ6z7eq6nDCHA7z+8nZHZOF988SJSapPhz394L64yqVt63U7u3tbAj46O8PTJMXY0B3n79a22vHdtpbEyfSCvAF2uklFd4OT0yFy+5LEYX/jJBSYjSb7wtp1rWtXZCte3V/Otg/1kshLnZXOCI/3aVN/ODOG2pnmlUd3ntxAT4STtNcs/11hqjN4Z/hloAd4KPAd0AIWLfo3xCPDl3M9fBt519QZSyjNSyrO5n4eAMaC8GrgKhaKstFZ5SWaypq0A5k3pl3/Vbr6vavEJyly89Ayhy+mgvdZH35S9PYQD01qAuaF2bWcIa3MZwkmDAY3OdO78LLXcbS1QV+kx3IdZvgyh7mFq/TqIJjP41lFPkMvp4NPvu4nX/vghnv/D+3nyP9y36CTYDn73wW0k0hlCsRT/8+dvuGKCXwo1PuNqwXpAqIvR2E1XUxC3U3B8KFR8Y7Rs0+efv8Dbdrdw88aVYUa/kriho5poMsPZsStDijf6pvG5tLJSu9CtJ4qJAo3PxWlcAXoFS43Ru+M2KeV7hBCPSCm/LIT4GvB4CfttllIOA0gph4UQi0ouCSFuBzzA+QWe/xi5nsbm5mZ6enpKGFp5CIfDK3JcCntQx9cYk6NakPTDZ15gU7Xx1fpjE9rrhs6fpGf6TFnGthiXH9+hYW0sTz3/Eh3BhdfUDuS2O/nmIUIXrK/KB4hz/NKIrefXy6eTuAQce/1lHGt4xXpgTusdfOG1wyT7C3/cFbp238idpxdPvokcWj9ZpUI40zHOD8QMnX8HziYRwLGDL3PSpmAAYGw6J1Ty/AEGG80FdfrxDccSTI0O0dNT2J9vrbNUKoD/+Q43UsL0+cP0FJyxmScZjjMcyhY8B6++fg+d1wLHI6+9hMvGc/ByWv2CF45dosc3UnTbr55MEE1muK92Vs0RCpCNaffof378AA93agt4UkqeeDPG9mrJ888/Z+v+gh54/shZtmX6Cj6fzkomwkliU8Pr7l5h9M6qL4XPCCH2ACPApsVeIIR4Ci2reDV/bHh02vu0omUoP5Kzu7gGKeWjwKMAe/fuld3d3WZ2sST09PSwEselsAd1fI1RNzDDZw+9SOu23XTvNi59PvH6ABw8wtv230lnvf0KecW4/Pg6z47zuSOvsn3PTdy+eWFJ9aEDfXDkKA/cuy/vwWiFJ6eP8u9Hhti/f79t5UbfHnqDDfUhHri/25b3W6mMzyX4kxeforVzG937NhXcptC1O/JqHxw6ysP771rzfZbF+Eb/65wfD9Pdvb/otv8+doSW6gkefOB+W8ewYzbGXxx4hoaN2+m+w5woR09PD/v37yfx+I/YvnUT3d07bB2bovw8NXOUs28OF/yMvfr6/Un4BP7ePh6y+Ry8nDvGj2gG80Xuyf1TUXqe7OF9t23ggz9zQ9nGs9r5zNFnGRdBurv3AnBhPMzk48/x9i0Vts+rdp1+mUhW0t19V8Hnh2Zi8MQz3HHDTrpXsABQOTC6bP1oTvjlT4DHgBPA/1jsBVLKh6SUewp8fR8YzQV6esBXUKBGCFEF/BD4EynlKwbHqlAoVih5c3qTpV96KVrTCijjMOrNFs6pjJbSQwha30MonmY8bK7scTEGpmN0rPFyUZiXrJ+KmBOVmcqVp613lVGAuoDxktGRUIyWavuv0aagF6dDMDhjrZc2nsoiJfjWUQ/hWqLW72E2lrrGV7EQs7EUNWUSlNHZ1VbFZCTJ2Nzi9+RPP3kGhxB8/KHtZR3PamfflnpeuTCZV279P8e0zOv1DfZfr9uaApwdneMya/UrmC97X/72lKVm0YAw5zcIcFJKOS2lfF5KuSWnNvqPJez3MeAjuZ8/Any/wL49wHeBr0gpv13CvhQKxQqhvtKDx+UwbTI9FopT5XWtiAmdUW+2cDyNEFBZYt9SV5MmXHBu1JoZciH6JiNrXlAGNMn6ahOiKDrTkSQ+t3NFnG/LTX2l1r9lZDI+PBuntQwBodMhaKnyMmTReiKa1EqA/etEZXStUe1zk5XzfdmLMRNNUV3mhZzdbZry5fGha9UxdU6PzPG9w4P80t2byrJIspZ4+w2thOJpHj8+SiYr+dqBPvZtqafRb78AUleRBdZ5vYL1d8yK/bd/Off9szbv91PAw0KIs8DDud8RQuwVQnwht817gfuAXxJCHM593WTzOBQKxRLicAg6an30mxRJWSmWE6AJHEDxDOFcIk3A4zKlploIXdXuXJFGeKPMRlNMR1Nsblj7ASFooihmRYwmI8l1rzCqU1fpISuLn+9SSoZn4iWVRy+G5kVoTVQmmtQyD+vNaHqtUKv7YcaKX8ehWIpqX3mP83Wt2iLd8cGFhWUeff4CXpeT39y/taxjWQvcs62BjXV+PvP0Wf7ihycZnInxkbs6y7KvrubFF1jH5sojjLUaKBYQnhRCXAJ25rwA9a+jQog3re5USjkppXxQStmV+z6Ve/yglPKjuZ//RUrpllLedNnXYav7VCgUK4MNtX4GTJZ+jc7FV4yJetDrQoh5NbuFCMfTJSmM6jQFKwhWuDhrU4bw0mQEYFl6MZeDukrjCoU60yogzKP/H4oF1aFYmlgqU5YMIWhKo1a9CGMpLSBUGd/ViV76PW3Ai3Amlswv2pWLoNfNpno/J4YLB4SjoTiPHRnkvXs7qFFl50VxOAT/+R27uDgR4Z9evMgjN7XxVhMaA2bIW08ssMA6GorjdAjq1+H9f9HZipTyA0KIFjRF0XcuzZAUCsVaZkOdj8P95uxER2fj3Ll1ZRj6OhyCYIXLQA+hdc+0yxFCsK05wLkxewPCzTbKea9kav2evM2GUaaiKWU5kaMhoC3EjM8l8pOpQgzNasFauTKEbTU+Rt4cvsavzAjzGUIVEK5G9KCqWJm+tk0qX9ZfTna3VXNkoPDn2JdfukQ6K/mVezaXfRxrhQeva+b7v3U3E+EE92xrKJtfY1OwgqB34QXWkdkEjYGKkit7ViNGCnTHgaNSyt6rv8o9OIVCsfboqPUzG0sRihsT+shmJWNzCVpWUAlHjd9T1Cg5nLAnQwiwrTHAWZsCwosTEYSAjWX2JFsp1FvIEE5FEutyhbgQuvenXkq1ECOz2vPl6pdqq/GRzkrGiwh5FELvIVQZwtWJniEs5v0KOVEZf/kDwls6axmYjl2TtY4k0nz1QB9v2dW8bqow7GJPezXdO5pwOe3vHdQRQmjCMmOFrdQHZ6K0rwPBtUIU/a9LKTNAQ07kRaFQKEpiQ07MZMBgH+FkJEk6K1dUY361z100QxiK25MhBK2PcCKcMLRCXoxLExHaqn1414nAhq6SuZCqXCGmI6l839J6pym3EFMsENMzhLqJvN3o9h9W+ghjqodwVVNvsGw5nsqQSGepXoKA8K5cxcrL56/0qvvma/3MxlJ87D7VO7hS6WoKcG4sUvC5/qkYG1RAuCi9wItCiD8VQvy+/lXOgSkUirXJhjrtZttvsIxvJVlO6BgJCMPxFEGbMoR5pVEbsoTnxyNsaVw/K9d1fg+pjCScKK5QCJBIZwgn0tRVln9SuRqo8rqocDmKSuyPzMZxCGgMlKfXV1+1t9JHqEpGVzdVXjdOhyiqFqzfk5eiZHRHc5Bav5sXz0/kH0tlsnzxhYvctqmWWztryz4GhTW6moJMhBNMX7XAkMpkGZ6NrZvqmasxGhAOAT/IbR+87EuhUChMoWcI+6fMBYSrLUNoVw8hzDfCl1o2ms1Kzo+H2dq4cC/YWkMXRTHqpTed8yysq1wZIkbLjRCCpqoKxkKLl4wOzcRprvKWrdxLF6uxEhDqGULfOsmKrzUcDkGtv7gfpl5SWm5RGX1M3TuaePLEKPGcaNEP3xxmcCbGbyhl0RXNQsIyQzMxshI61mlAaGi2IqX8r+UeiEKhWB/U+N0EKlwMTBub2I2sQKPYar+b2SL9LLOxFFVee1aq22t8eN2OkjOEw6E40WRmUXGQtcblKplGenr0SafKEM7TFPQWzxCWyZReJ+h1U+V1WSoZzfsQqgzhqqUh4GEyXCwg1J5figwhwHtu7eC7hwb58bER3ranhb954jQ7W4Lcv6NpSfavsEY+IBwLc9umuvzjuh3WhnXg0VsIQwGhEOJZ4JoGDCnlA7aPSKFQrGmE0LwIjSo/js7GEWUsRbOCniGUUhZUQ0ukM8RTWdsmJg6HYKsNwjJ6QLkeA8Kry4MWQg8IVQ/hPE3BCs6MFhZh0BmejXNdS1VZx9FW47NWMppSPYSrHSN+onrVxlKIygDcuaWerqYAf/7Dk/z7kSEGpmN8/dfuXJcKlauJ9hofPrfzGqVRvY1Fb2tZbxit7fgE8Ie5rz8FDgMHyzUohUKxtukYV4gkAAAfBElEQVSo9Rs2px8NJWgIVJRVecws1T436azM9yZdTb6XxcaJSVdTgHNFJuXFWM8BoVFz+qlclqE+oAJCneaqxTOEuil9ucu6NXP6xUtXCxFLZhACvO6Vcw9RmKOu0kDJ6BL2EIK2UPe5X7gFkDx7eow/eft17Fsh9kiKhXE4BFubKq8pGT0/FqbC5Sibdc5Kx2jJ6OtXPfSiEOK5MoxHoVCsAzbU+Xjp/MSCGbbLGQnFV5TlBEBNbsIxG0tRWaBPMFSGiUlXc5DvHR4qqTfx3FiYGr97XVkq6IFdsXIznamwFvioDOE8TVUVzMXTRJPpglm2cpvS67TV+DjYO236ddFkBr/bWTZvM0X5qa/0MBlevGw5VIaFuGJ0NQc58H8/RDyVKfhZoFiZdDUFOXDhSoXYkyMhdrQETfucrhUMLZcJIeou+2oQQrwVaCnz2BQKxRplQ62faDJjSOhjNBRfUf2DMB/oLeSLNRvTepaqbAwIdSGY8yWUjZ4fC7OtMbCuJsZ+j4tKj9Owf93YXAJXTsRCoZG3fFig71fv62urKe/KeluNj9lYyrBirE40mcGnykVXNXWVFYTiaVKZ7ILbzERTOB2C4BIHZk6HUMHgKmNbU4Ch2Xi+mkdKycnhubKXva9kjNZPvI5WIvo68DLwB8CvlmtQCoVibbOpQWvavjRZvI9QCwhXVoaw+rIMYSHKkyEsXWn03Hh4XZWL6jRVeYsaq+uMzSVoDFaoPqDL6NCVgRfo++2b0jy9yi3XrnscDpvsI4wl00pQZpWjZ/oX6wWeiSWp8rrW1YKXwho3dtQAcKR/BtDu+1ORJNe1rl8DBUMBoZRys5RyS+57l5TyLVLKF8o9OIVCsTbZ3KAFJRfGFw9u4qkM09HUyisZzWWPFjKKL4cfVmedH4/TwVmLfYRTkSRTkeS6DAgbgxVFVTJ1xuYSNAVXVkZ6ucl7hy7Q99ubW9jZWF/egLCj1po5fTSZUQHhKkcvc59YpPR7NpbO35sVisW4cUM1QsAbfVoJ+tGBWQCua1UZwoIIIW4TQrRc9vuHhRDfF0J8RghRt9hrFQqFYiE21PpwOQQXJyKLbqeX+a20DGFDQJ+cFA4yyhEQupwOtjRWFlV7XAhdUGbrOgwIm4IVxktGQ3EagyvrfFtuGgMVVLgcCyoD905FqfW7bbNZWQi9JHXIpLCMVjKqAsLVTGNukWZ8kT7CmWjS1jJ9xdol6HWzoznI67me5GdPj+H3OLlxQ80yj2z5KJYh/EcgCSCEuA/4FPAVYBZ4tLxDUygUaxWX08HGen/RgDDvQbiCTOlBU7wTAsYXWK0uR0AIsL05yJlRayWjp0dC+fdYbzQFvUWN1XXG5xI0rbCe1eVGt4pZKEPYNxllowGPx1JpCnpxOgSDM8Ysa3SiqmR01dOUW6RZbGEnFEvlBb8UimLcsbmOVy9OMR1J8vTJMe7rasTrXr/3iWIBoVNKOZX7+X3Ao1LKf5NS/imwrbxDUygUa5ktDZVcGF88IBxdgab0oAW0tX7PohlCv8eJ22arjO3NAQZnYkRMimoAHB2cpa7SQ9sKC66XgqaqCiLJTNH/WyqTZTKSXFGelyuFDXX+BXsIe6cidJa5fxA08Y6WKq+1DKFbiX6sZvQM4WK9wDOx1JJ5ECpWP++7bSOJdJaf+ewLjITivHVP83IPaVkpGhAKIfS76IPAM5c9p+6uCoXCMlsaA1ycjJBeRDVuODfxa61aeb5ADQEPEwusVs/GUmXxwurKZfesCMscGwyxu61qXQouNOUnk4uXjeoBvsoQXsum+kouTkTIZuUVjyfSGYZm4nSWuX9QR/MiNCkqk1I9hKsdn8dJsMLFWGixktHy3HcVa5NdbVU8sLOJwZkYeztreeeN7cs9pGWlWED4deA5IcT3gRjwEwAhxDa0slGFQqGwxM6WIMl0dtGy0f7pKEGva0l9pYzSEKhYNENYjomJXu5pto8wnspwZnSO69urbR/TakAvNxstUjaql6M1qR7Ca9jREiSazFwTjJ0fi5DJyiUrRW6r8TKkRGXWJY3BigV7CDNZSSiuAkKFOR79xVv52kfv4NEP7123/oM6iwaEUsq/QLOY+BJwj5RSXxp0AL9T3qEpFIq1jK7mdWI4tOA2fVPRskvZW0ULCAv3EM5Ek2UpXdpY56fC5eDMiLmA8MzoHOmsZM86DQhbcmWyI7OLB4R6KWK5DdZXI3rAd/qqc+/0qHb97mhZqoDQx8hsnMxVmcrFiClRmTVBY7CC8QUyhDPRJFLOq5EqFEZwOR3cta2BOnXeFLedkFK+IqX8rpQyctljZ6SUb5R3aAqFYi2ztTGA2yk4ObxwcNM/FWVD7UoOCAtPTsbnEmVRqnQ6BFsbA5wxWTJ6dFAr6FivGcK8sXqRzJKuorlSz7nlZHvOB/P0VdnpUyNzuJ2CzQ3lF5UBLSBMZ6Vh1VgppRKVWSMs5ic6mfMnrFf9vwqFJexVPFAoFAqDeFwOtjUFF8wQZrOSgelY2b3NrNIQ9BBNZogmrxUqGZ9LlE2YZEdL0LQX4bHBENU+d97Hbb3h8zipr/QwMF0sIIwRrHBR5VMt8lcT9Lppr/FdkyE8MzKXW9xZmumE0eBeJ5WFrAS/Rx3T1U5jYGH7GL2fu0EFhAqFJZYlIBRC1AkhnhRCnM19r11k2yohxKAQ4u+WcowKhaL83NhRzeG+6WuEKkDzm0qks2xYoUGMHvBdLXIQSaSJJDN5VTy76WoOMDwbJxRPGX7NscFZ9rSvT0EZnbYaX9Hes4HpGO21vnX9f1qMPe1VHO6fyf8upeTYUGhJzZzba3UvQmMBYTKjffetYzn5tUJLtaYWXOjeN5HLEOoesQqFwhzLlSH8JPC0lLILeDr3+0L8d+C5JRmVQqFYUm7bVEconubM2LUZr97JXPneCu0h1CemV2ed9DLScgWE25tySqMG/QiT6SynR+bWbf+gjhF1yoHpKB2qXHRB9nbW0TcVzZftDc3GGZ9LcNMSmjnr/Z1GM4SJjLbYpEpGVz96KXf/1LX2JypDqFCUxnIFhI8AX879/GXgXYU2EkLcCjQDTyzRuBQKxRJy++Y6AF67OHXNc3qv0ko1UtcnJwNXebPpJU1lCwh16wmDZaNnRudIZrLsaVvnAWGtj8HpGPPaaFcipWRwOrZuy2qNcEunVszzRq+WJTzcp31fyoAw6HVT5XUZzhBG0/OvU6xu9MXB/qlrj/1kJIHTIZTKqEJhkeUqqm+WUg4DSCmHhRBNV28ghHAAfwv8IpoH4oIIIT4GfAygubmZnp4e2wdcKuFweEWOS2EP6vhaQ0pJnVfwvVdOsSFx6YrnnjmewO+C04de4cwyl/AVOr6ZrMQh4IXDp2iJXsg//tqINgPtPfUmPUP2ZyWyUuJxwtOvn7xivwvxXL9WXhUdOEXP9Bnbx7NaiE2kiKUy/ODJHoKe+fNJP7bhpGQukSY+OURPz/gyjnTlkspKPA74es8RvBOn+PdTCVwOGD97iJ7zS3eNVruzvHlugJ6eiaLbToWigODimeP0TJ0u/+AUZSOS0hZznn3tKN6JU8D89Xv0bIKgG55/XhWUrRXUvGppKVtAKIR4Cmgp8NQfG3yL/wv4kZSyv1g/h5TyUeBRgL1798ru7m4TI10aenp6WInjUtiDOr7WefvsMf719QHuuOveK6ThP3PiRa7f4OD++/ct4+g0Fjq+7QefwRGspbv75vxjfS9fgsPH+an77ylblnDHsReIud10d99RdNunvneUYMUQ7/mp+3GsY5+l7KlRvnbqIG07buLWzvm2df3YvnpxCp55mbfuu5HundesUSpyPDJ5hB+8OcxnfvVu/strL3DbpmoeeuDOJR3D9kuvMTQbp7v73qLbHv7WU0CCu2+/lZs3LihXoFglfPLFx6moa6W7ew8wf/3+S+9rtGaMnROK1YGaVy0tZSsZlVI+JKXcU+Dr+8CoEKIVIPd9rMBb7AN+WwhxCfgb4MNCiE+Va7wKhWJ5eOvuFmKpDM+dmb8NZLOS0yNzSypWYYUNtX76C5SMOgRl9TXa3hw0bE5/dDDE7vaqdR0MgmZzAnB+AcsO/f+5fYn89FYrH7lrE7FUhoc+/RyXJqP87M3tSz4GIwJBOjFVMrqm2FDrL9hDOB5OKkEZhaIElquH8DHgI7mfPwJ8/+oNpJS/IKXcKKXcBHwC+IqUcjHxGYVCsQq5Y0sdrdVeHn3+Qr6/683BWSLJDDd0rOy+N21ycuXE9NJklNZqH84yBmDbmwOMzSWYjS6uNJrOZDk1HGL3Ou8fBOio9eNxOTg/XjggPDs6R6DCRZsypV+UPe3V/O6DXYzPJQh6Xbzt+kKFQOWlrcbHbCxFOHGt5cvVxNLaPaXKq2wn1gIb6/z0FggIx0LxslVkKBTrgeUKCD8FPCyEOAs8nPsdIcReIcQXlmlMCoViGXA7HfzW/dt4o2+GfznQB8Bjh4fwOB08eF3zMo9ucbY1BZgIJ67wxjo9EmJnmbNMurBMIXXWy7k0GSGRzrJrhWdalwKnQ7CloXLBgPDMaJhtTQFlOWGA3394O8/9YTcv/McHqFqGzFtbjRa0DxvIEkZzAaHKEK4NtjUF6J2Mkkhn8o8l01lGQvG80JdCoTDPsgSEUspJKeWDUsqu3Pep3OMHpZQfLbD9l6SUv730I1UoFEvB+27bwAM7m/jT7x3jF794gG++1sf+HY0rXjFOz2C+OaCpLSbTWS6MR9jZWt6AsKtZK38sVjZ6fCgEwK42FRCCVjZ6rkDJaDYrOTUSYnvu/6ooTmd9JdX+5bk+dSVYI9YTsRS4nQKve7nWvxV2sqMlSCYrr7iOh2djSDlvBaRQKMyj7pAKhWLZcTsdfO4XbuHjD3XRPxXl5o21/Jd37l7uYRVlT3s1DgFHBmYBOD8eJp2V7GgpbwDWXuOj0uMs6kV4YjiEx+nI98+td3a0BOmdijIbu7LU9szYHNPRFLdtqlumkSnM0Fajm9PHi24bS0uCXrfK/K4R9OqL0yPzi2GDOS9YZRmjUFhHFdUrFIoVgdft5OMPbefjD21f7qEYprLCxbamAIf7tQzh0VxgWO6SUSEE25qDV0yKCnFyeI6u5gAel1r7A7i1sxYp4VDfNN075pVEXzo3CcBd2xqWa2gKEzQFvTgdgsGZa3vJriaalgRV/+CaYVNDJR6n44p730AuIFQlowqFddQsQaFQKEpg//ZGXjo3wVgozrcO9rOp3s+2JcjI7WgOcLZID+GJodCKV2pdSm7aUIPTIXi9d/qKx58/O05nvZ/2GpVhWA04HYLWam9Bg/KriaZRAeEawu100NUc4OjgbP6xgekoDgEtShBKobCMCggVCoWiBH7hjk7SWcmv/fPrHOyd5kN3di6JxcP25iAT4SRTkWTB58fm4kyEE0pQ5jIqK1zsaq3i5fOT+cfGo1mePzPO269vXcaRKczSWe+nr4Da5NXEUpJgxcruRVaY47ZNdbzRN00ynQWgbypKS5UXt1NNaRUKq6irR6FQKEpgU0MlH3+oi0sTEd5xYxu/cEfnkuy3S1caXUBY5oQSlCnI2/a0cLB3mksTEQB+fEnrJ/zQnUtz3BT2sLGu0lhAqEpG1xx3bqkjnspydFAr1T/cP8MuZa2jUJSECggVCoWiRD7+0HaO/Oe38NkP3IzP41ySfeqKmGcXCgiHtYBQlYxeyXtu7cDpEPz1E6f59sF+nulL8wt3dOaFShSrg856P1ORJHPxxb04tZJRlSFcS9y+uR6A589MMB3Pcmkyyp1blCCUQlEKKiBUKBSKVUhLlZdghYszCyiNHh8K0VHrW/HWHUtNU5WXjz/YxQ/fHOYP//VNNlU7+KOf2rncw1KYpLNOExDpnVw8S6gyhGuPukoP93Y18I3X+jg6ofkR3pELEhUKhTXUXVKhUChWIUIIupoDi5aM7lblogX5nQe72L+jkZloitTAMQIV6qNwtbGxXgsI+6ai7GkvXC6YTGeJpbUAQrG2+JW7N/PLX3qNLx2HjXV+VRqvUJSIyhAqFArFKmVHS5Azo3NIKa94PJxIc3Eiwh7VV7MgN3TUcN/2RpxLIACksJ/O+kpg8QzhTFQTXKpVAeGao3tHI5969/Xc0uTka792h7qOFYoSUQGhQqFQrFK6moJMR1NMhK9UGj2Z6x/c3a5WzRVrk0CFi/pKD31TkQW3mcwp8NargHDNIYTg/bdv5Ldv9tKh/AcVipJRAaFCoVCsUrbnlEavFpY5nvPo2q0yhIo1zMZ6/6IZQt2SRZWMKhQKxeKogFChUChWKbrS6NV9hMeHQjQEPDQFK5ZjWArFktBZpwJChUKhsAMVECoUCsUqpTFYQbXPzZmxK5VGjw2F2NVWjRCqr0axdtlYX8nwbCxvUH41KiBUKBQKY6iAUKFQKFYpQgi2NweuKBlNpDOcHZ1TCqOKNU9nnZ+shIHpwllCvYewRlmvKBQKxaKogFChUChWMdubg5wZDeeVRk8MhUhnJdcvIMWvUKwVdOuJ3qnCAeF0JEmlG1xONdVRKBSKxVB3SYVCoVjFbG8OMhtLMTaXAODFcxMA3LlFGTUr1ja6OX3fAn2EU5EkQbcqm1YoFIpiqIBQoVAoVjE3dGiZwFcuTALwk7MT7G6rUn1TijVPY7ACn9u5oLDMZCRB0KMCQoVCoSiGCggVCoViFXNjRw2NwQoePz7CWCjOG33T3NPVsNzDUijKjhCCjXV++hYoGR2bS1BdoQJChUKhKIZruQegUCgUCus4HIKHdzXzrwcHmImmyGQlH7x943IPS6FYEjQvwmvN6aWUDM3E6GpX694KhUJRDHWnVCgUilXOb+7fSsDr4qXzk7x37wY66yuXe0gKxZLQmcsQ6qJKOlORJPFUlnqvmuYoFApFMVSGUKFQKFY5G+r8fPs39jEwHePebapcVLF+2NRQSTyVZXg2TluNL//40EwcgHqfKhlVKBSKYizL0pkQok4I8aQQ4mzue+0C220UQjwhhDgphDghhNi0tCNVKBSK1cHWxgD7tzficKgJsGL9sKMlCMDpkbkrHh+c0foK673qelAoFIpiLFctxSeBp6WUXcDTud8L8RXgr6WU1wG3A2NLND6FQqFQKBQrnO3NWkB46pqAUM8QqpJRhUKhKMZy3SkfAb6c+/nLwLuu3kAIsQtwSSmfBJBShqWUhaXEFAqFQqFQrDuqfW7aqr2cGgld8fjQTAyf20nAvUwDUygUilWEuLoRe0l2KsSMlLLmst+npZS1V23zLuCjQBLYDDwFfFJKmSnwfh8DPgbQ3Nx86ze+8Y1yDt8S4XCYQCCw3MNQlAl1fNc26viuXdSxXf38r9fjTMay/Pk9/vxjf3MwzmxC8kc3ZtTxXcOo63ftoo5t6dx///2vSyn3Gtm2bKIyQoingJYCT/2xwbdwAfcCNwN9wDeBXwK+ePWGUspHgUcB9u7dK7u7u80PuMz09PSwEselsAd1fNc26viuXdSxXf28kTzN3z17jr377iFQ4UJKySdeeIruHU0EAtPq+K5h1PW7dlHHdmkpW0AopXxooeeEEKNCiFYp5bAQopXCvYEDwCEp5YXca74H3EmBgFChUCgUCsX6ZO+mOrISDvfNcE9XA2NzCSbCSXa3VUFqermHp1AoFCue5eohfAz4SO7njwDfL7DNa0CtEKIx9/sDwIklGJtCoVAoFIpVws0ba3AIeO3SFADHh2YB2N1WvZzDUigUilXDcgWEnwIeFkKcBR7O/Y4QYq8Q4gsAuV7BTwBPCyGOAgL4/DKNV6FQKBQKxQok6HVzXWsVL56bAOD13mmcDsGutqplHplCoVCsDpbFmF7K/7+9e42Rq6zjOP79SSmBAHIrUgEpJWJEQC4t8QqCRKFR0EiUBA2J+MIm3DRGUV7oO7lF4js0YKKRqCiovJBQIICvuFmhBSpQTAlIuccA1qCVvy/mLF260+1ud3bn7JzvJ3nSM+fynOfML505/50zZ+pl4JN95j9A70YyY49vA46ew6FJkqR5ZsVRi7ny1sd4+pVN3PXYixz/nr3ZfZehnOJI0rzjD/RIkqR57fPHHkgCP7xlHY88+yonvW/R9jeSJAEWhJIkaZ5791678qVlB/Ontc8BcPqR/W5yLknqx+spJEnSvPf9z36APXfdmdOOPICli/z9MkmaKgtCSZI07+26cCe+t+L9wx6GJM07XjIqSZIkSR1lQShJkiRJHWVBKEmSJEkdZUEoSZIkSR1lQShJkiRJHWVBKEmSJEkdZUEoSZIkSR1lQShJkiRJHZWqGvYYBirJi8BTwx5HH/sBLw17EJo15jvazHd0me1oM9/RZr6jy2xn7pCqWjSVFUeuIGyrJA9U1bJhj0Ozw3xHm/mOLrMdbeY72sx3dJnt3PKSUUmSJEnqKAtCSZIkSeooC8K589NhD0CzynxHm/mOLrMdbeY72sx3dJntHPI7hJIkSZLUUX5CKEmSJEkdZUEoSZIkSR1lQdhHktOSPJZkfZJLxs0/v5lXSfabZPtDk9yb5Ikkv0mysJl/YpLVSTYnOWuS7X+W5IUkD281/8okf0uyJsnvk+w1iOPtmrbmO275t7Y3BvXX5myTXNCM7ZEkV8z0WLuorfkmOSbJPUkeTPJAkhMGcbxdM8x8kxyc5M4k65r/oxeNW7ZPktuafm9Lsvcgj7sLWpyt51UDMIv5fjPJo00+dyQ5ZJr779uv+qgq27gG7AQ8CSwFFgIPAUc0y44FlgAbgP0m6eMG4Oxm+hpgZTO9BDga+AVw1iTbnwgcBzy81fxPAQua6cuBy4f9fM231uZ8m2UHA7cCT002Btv8yhY4Gbgd2KV5vP+wn6/51lqe7yrg9GZ6BXDXsJ+v+daGnS+wGDiumd4DeHzc/q8ALmmmL8H33lHK1vOqdud7MrBbM70S+M0099+3X9vE5ieEE50ArK+qv1fVf4BfA2cCVNVfq2rDZBsnCXAK8Ltm1s+BzzXbb6iqNcCbk/VRVX8GXukzf1VVbW4e3gMcNNWD0ltam2/jauDbgHd7mr42Z7sSuKyq3mjWe2GqB6W3tDnfAvZspt8JPDuVA9LbDDXfqtpYVaub6deAdcCBzeIzm/7e1q+mrLXZel41ELOZ751VtamZv618+u5/sn41kQXhRAcCT497/Axb3hSmYl/gn+NeYKa7/VR9FbhlFvodda3NN8kZwD+q6qFB9NdBrc0WOBz4eHPpyt1Jlg+o3y5pc74XA1cmeRq4CvjugPrtktbkm2QJvU827m1mvauqNkKvuAD235F+O6zN2Y7nedWOmat8z6N/Ptva/1ydj4+EBcMeQAulz7zpfFoz0+23v4PkUmAzcP0g++2IVuabZDfgUnqXr2jHtDLbxgJgb+BDwHLghiRLq8pPgqeuzfmuBL5RVTcm+SJwHXDqgPruilbkm2R34Ebg4qp6dbrbq6/WZ+t51YzMer5JvgwsA06axvazfj4+SvyEcKJn6H2Pa8xBbOfynyS3NjcTuBZ4CdgryVixvd3tpyPJucBngHM8mdwhbc33MOBQ4KEkG5p+Vyc5YAB9d0Vbsx0b203Vcx+9y5u8adD0tDnfc4Gbmunf0ruESdMz9HyT7EyvYLi+qm4at+j5JIubdRYDXvI9PW3O1vOqmZvVfJOcSu8P5meMfe1iivuf1fPxUWNBONH9wHubOxMtBM4Gbp5sg6r6dFUdU1Vfa15M7gTG7nZ1LvDHQQwsyWnAd+j9p9i0vfXVVyvzraq1VbV/VS2pqiX0XuCOq6rnZtp3h7Qy28Yf6H2XgSSH0/vi+0sD6rsr2pzvs2z5y/UpwBMD6rdLhppv832j64B1VfWjrRbf3PQ37X4FtDhbz6sGYtbyTXIs8BN6+WzrDzF99z/Lr/mjp1pwZ5u2NXp3iXuc3l2LLh03/0J6J+qb6Z0AXLuN7ZcC9wHr6f21eOzOgsub7f8FvAw8so3tfwVsBP7brH9eM389veukH2zaNcN+ruZja2u+W62zAe8yOjLZ0isAfwk8DKwGThn2czUfW4vz/RjwF3p3t7sXOH7Yz9V8bMPMt8mwgDVseY9d0SzbF7iDXqF/B7DPsJ+r+dZanK3nVe3O93bg+XH53DzN/fft1zaxpXnCJEmSJEkd4yWjkiRJktRRFoSSJEmS1FEWhJIkSZLUURaEkiRJktRRFoSSJEmS1FELtr+KJEndlmTspwcADgD+B7zYPN5UVR8ZysAkSZohf3ZCkqRpSPID4PWqumrYY5Ekaaa8ZFSSpBlI8nrz7yeS3J3khiSPJ7ksyTlJ7kuyNslhzXqLktyY5P6mfXS4RyBJ6jILQkmSBueDwEXAUcBXgMOr6gTgWuCCZp0fA1dX1XLgC80ySZKGwu8QSpI0OPdX1UaAJE8Cq5r5a4GTm+lTgSOSjG2zZ5I9quq1OR2pJElYEEqSNEhvjJt+c9zjN9nynvsO4MNV9e+5HJgkSf14yagkSXNrFXD+2IMkxwxxLJKkjrMglCRpbl0ILEuyJsmjwNeHPSBJUnf5sxOSJEmS1FF+QihJkiRJHWVBKEmSJEkdZUEoSZIkSR1lQShJkiRJHWVBKEmSJEkdZUEoSZIkSR1lQShJkiRJHfV/Oq9kdoHHWz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,4))\n",
    "\n",
    "ax.plot(area.time, surface_elevation_ele300[0,:])\n",
    "\n",
    "ax.grid()\n",
    "ax.set_title(\"Surface elevation for element 500\")\n",
    "ax.set_ylabel(\"Surface elevation [m]\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "## 2. Plot Items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dfsu` objects support some quick plotting methods, through `plot_item(...)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set timestep to zero since inundation file is an aggregate over the whole model run\n",
    "# the dfsu file still considers it to have a timestep, but only one, so setting to 0\n",
    "# will access the first (and only) timestep of the file\n",
    "\n",
    "plot_dict = dict(levels = np.arange(0,1.5,0.1))\n",
    "\n",
    "fig_cur, ax_curr, tf_curr = inundation.plot_item(item_name='Maximum current speed', tstep=0,\n",
    "                                                 kwargs=plot_dict)\n",
    "\n",
    "fig_cur.set_size_inches(10,10)\n",
    "ax_curr.set_aspect('equal')\n",
    "\n",
    "fig_cur.colorbar(tf_curr)\n",
    "\n",
    "ax_curr.set_title('Maximum Current Speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surface elevation at timstep 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dict = dict(levels = np.arange(-1,1.4,0.1))\n",
    "\n",
    "fig_se, ax_se, tf_se = area.plot_item(item_name='Surface elevation', tstep=400,\n",
    "                                                 kwargs=plot_dict)\n",
    "\n",
    "fig_se.set_size_inches(10,10)\n",
    "ax_se.set_aspect('equal')\n",
    "\n",
    "fig_se.colorbar(tf_se)\n",
    "\n",
    "ax_se.set_title('Surface elevation; t = 500')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------\n",
    "## 3. Calculate Max Amplitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum amplitude can be calculated in two ways:\n",
    "\n",
    "1. Calculated from maximum water depth in the `inundation` output\n",
    "2. Calculated from surface elevation over range of timesteps from `area` output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Method 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_amplitude_1 = inundation.max_ampltiude()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Method 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_amplitude_2 = area.max_item('Surface elevation', node=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These methods output at the `mesh` nodes, but have an option to output at the elements.\n",
    "\n",
    "Compare methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dict = dict(levels = np.arange(0,2,0.1))\n",
    "\n",
    "fig_ma1, ax_ma1, tf_ma1 = area.plot_item(node_data=max_amplitude_1, kwargs=plot_dict)\n",
    "\n",
    "fig_ma1.set_size_inches(10,10)\n",
    "ax_ma1.set_aspect('equal')\n",
    "\n",
    "fig_ma1.colorbar(tf_ma1)\n",
    "\n",
    "ax_ma1.set_title('Max amplitude - method 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dict = dict(levels = np.arange(0,2,0.1))\n",
    "\n",
    "figma2, axma2, tfma2 = area.plot_item(node_data=max_amplitude_2, kwargs=plot_dict)\n",
    "\n",
    "figma2.set_size_inches(10,10)\n",
    "axma2.set_aspect('equal')\n",
    "\n",
    "figma2.colorbar(tfma2)\n",
    "\n",
    "axma2.set_title('Max amplitude - method 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get similar outputs from both methods. Differences can potentially occur by having large timesteps in writing the area output so that some maxima are missed.\n",
    "\n",
    "All mesh elevation values in the `inundation.dfsu` will be adjusted for any datum shift used in the model. If this is in the case, the `max_amplitude` method accepts a `datum_shift` input to account for this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------\n",
    "## 4. Create New Dfsu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New `.dfsu` files can be created for any analysis performed on an input `.dfsu`. The new `.dfsu` will use the underlying `mesh` of the original `.dfsu`, so for any analysis performed it is important that the order of the data at the `mesh` elements is maintained.\n",
    "\n",
    "Using the `dfsu` method `create_dfsu()` new `.dfsu` files can be created for non-temporal and temporal layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Item type and units**\n",
    "\n",
    " There is the option to specify the `item_type` (ie. water level, velocity, flux, etc...) and the `unit_type` (ie. meters, feet, m/s, etc...) that the layer will have associated with in the `.dfsu` file. These are provided by using the MIKE21 integer key.\n",
    "\n",
    "Within the `dhitools.units` module a list of available items and units can be found with `units.available_items()` and `units.available_units()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AdsorptionCoefficient',\n",
       " 'Age',\n",
       " 'AgriculturalArea',\n",
       " 'AgriculturalUsage',\n",
       " 'AirPressure',\n",
       " 'Angle',\n",
       " 'Angle2',\n",
       " 'AngleBedVelocity',\n",
       " 'AngleOfRespose',\n",
       " 'AreaFraction']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available_items = units.available_items()\n",
    "available_items[15:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AcreFeetPerDayPerAcre',\n",
       " 'AcreFeetPerDayPerSecond',\n",
       " 'Bar',\n",
       " 'CentiMeterPerSecond',\n",
       " 'ConcNonDimM3PerSec',\n",
       " 'CubicFeetPerSecondPerAcre',\n",
       " 'CubicMeterPerDayPerHectar',\n",
       " 'CubicMeterPerDayPerSecond',\n",
       " 'CubicMeterPerHourPerHectar',\n",
       " 'CubicMeterPerHourPerSecond']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available_units = units.available_units()\n",
    "available_units[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the correct name for the item and the unit is known the MIKE21 the integer key can be obtained by parsing the name in to `units.get_item()` and `units.get_unit()`.\n",
    "\n",
    "For example to get the key for item type \"AirPressure\" and unit type \"Bar\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(AirPressure, Bar) = (100409, 6107)\n"
     ]
    }
   ],
   "source": [
    "ap = units.get_item(\"AirPressure\")\n",
    "bar = units.get_unit(\"Bar\")\n",
    "\n",
    "print(\"(AirPressure, Bar) = ({}, {})\".format(ap, bar))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Non-temporal (ie. one timestep)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get max amplited data at the `mesh` **elements**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_amplitude_ele = inundation.max_ampltiude(nodes=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call `create_dfsu()` on the original `area` `dfsu` object parsing in the `max_amplitude_ele` data. Specifiy `item_type` as \"WaterLevel\" and `unit_type` as \"meter\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "area.create_dfsu(arr=max_amplitude_ele, output_dfsu=\"data/max_amplitude.dfsu\", item_name=\"Max Amplitude\",\n",
    "                 item_type=units.get_item(\"WaterLevel\"), unit_type=units.get_unit(\"meter\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Temporal**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get surface elevation data between timestep 150 and 170, again at the `mesh` **elements**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface_elevation_t150_to_t170 = area.item_element_data('Surface elevation',\n",
    "                                                        tstep_start=150, tstep_end=170)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call `create_dfsu()` on the original `area` `dfsu` object parsing in the 2-dimensional surface elevation data between timestep 150 to 170. Specifiy the `start_datetime` as the `time` at timestep 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "area.create_dfsu(arr=surface_elevation_t150_to_t170, output_dfsu=\"data/se_t150_to_t170.dfsu\",\n",
    "                 item_name=\"Max Amplitude\", start_datetime=area.time[150],\n",
    "                 item_type=units.get_item(\"WaterLevel\"), unit_type=units.get_unit(\"meter\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "## 5. Unstructured Mesh to Regular Grid to Raster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data at either nodes or elements on an unstructured can be interpolated to a regular grid at whatever desired resolution.\n",
    "\n",
    "Since the regular grid will create points spanning the entire rectangular domain of the model, we will receive points outside of our model domain. To remove these, we can generate a mask from the `mesh` file used for the model and use this to only select the regular grid points within the model.\n",
    "\n",
    "Once we have data on a regular grid, exporting to raster using a library such as `GDAL` is straightforward. \n",
    "\n",
    "First, we will load the `mesh` file and get the model mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_f = 'data/example_mesh_no_elevation.mesh'\n",
    "m = mesh.Mesh(mesh_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"299561.49737196346 6983870.554744213 315634.02738382353 431429.89650624804\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,14399171.005994674)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"2876.1993100416535\" opacity=\"0.6\" d=\"M 315540.38242775045,7394626.690214633 L 315849.09119219705,7389814.086870092 L 317241.4095877331,7385122.307470756 L 319856.85461408756,7380987.643442172 L 322738.1963065563,7377023.931632892 L 324063.64246742794,7372334.741840488 L 323308.33061134606,7367529.964561405 L 322611.83108986437,7362741.060363927 L 324205.54923050065,7358112.1643329915 L 326719.17151726206,7353769.395150135 L 329961.7635779813,7349942.334463283 L 333848.86034254555,7346766.860158124 L 338056.3575941463,7344025.78721706 L 342445.4752048825,7341585.780974973 L 347076.23592858383,7339650.828082775 L 352012.9121333196,7338805.774518534 L 356998.46536992537,7339330.580074717 L 361703.15053806605,7341006.824881067 L 366030.1105837335,7342005.431079786 L 370847.61931050394,7340541.098909273 L 372575.7862744358,7336315.7587642195 L 373534.00920104224,7331417.138437581 L 376405.4195184105,7327311.386623545 L 380392.3179527189,7324270.61412106 L 385360.3031155552,7323403.5024770135 L 389131.30193478387,7320316.36495704 L 391068.9114815197,7315699.711494797 L 392785.8324947744,7310980.0977571085 L 394551.5612200483,7306278.5323403 L 396040.14783950575,7301481.521885574 L 397502.8629609991,7297290.4141742475 L 398502.8457121805,7295117.531684032 L 398930.9424553024,7294187.3117215885 L 399359.0391984245,7293257.091759145 L 400258.2004938472,7293401.638576778 L 400685.90019891417,7289300.549683335 L 401162.1097951145,7286315.16481497 L 405309.77663281496,7286782.85854042 L 407778.060901037,7282480.328697274 L 409466.92577249766,7278088.603075364 L 410307.2672540907,7276481.055439919 L 412525.78745950246,7276799.133030231 L 415997.183338616,7273248.456196272 L 421238.54994240263,7269177.655269285 L 425311.20412062394,7266482.385587909 L 429504.4938328901,7263802.6271911925 L 434324.7515621118,7262640.9262332525 L 439774.2191793091,7260070.083746279 L 443076.273728875,7256536.769465856 L 445945.3656088177,7252695.787591055 L 447002.0882008913,7248001.062941064 L 446336.62213065225,7243943.969744213 L 447686.2633928828,7238510.886646665 L 449035.3384758018,7234125.900689891 L 451563.33126196463,7229829.501267848 L 452028.80412518163,7226118.243767113 L 455274.70268074336,7222695.883364828 L 458909.4694241233,7219378.445623793 L 459846.20190179197,7216936.611857507 L 461011.5644147936,7213622.054288239 L 463535.22685265157,7209445.361773807 L 466801.833045291,7206890.273529245 L 471185.52906625444,7205017.262461888 L 476120.3211478628,7204782.747059809 L 481093.3752001532,7204883.773952209 L 485323.91560078104,7202744.437866682 L 489126.1554676017,7200377.085014647 L 490006.8299916087,7195503.205205203 L 490277.71435844037,7190591.496351683 L 487253.1329729057,7189681.605033781 L 485320.01049373636,7187096.535320678 L 484353.0405745137,7183445.499894413 L 485924.3017853639,7176070.101363755 L 486156.23981868435,7171573.605030755 L 486119.0335989905,7166717.6130606225 L 488601.7160487577,7162644.041185788 L 489805.8483433169,7156762.055230898 L 489973.1154678234,7151460.761357708 L 493424.11819536396,7149966.007664165 L 496075.8046205946,7147815.627164654 L 498721.47743419895,7144340.451653445 L 500233.050439462,7140213.247589645 L 499037.3263813786,7136814.849756719 L 498984.2499128363,7135514.410645345 L 499297.85559264856,7132755.755197888 L 499825.05527789803,7128822.987597288 L 504389.4096727422,7127811.72024068 L 506211.62657287036,7132311.150521564 L 510040.0272320769,7132476.144515193 L 513992.54697760224,7129521.281236804 L 515648.0610274278,7126369.994047039 L 513758.3094354948,7121752.154257397 L 512116.341630972,7117039.763683897 L 510469.462775527,7112340.252569859 L 509045.5879322189,7107565.213194726 L 507859.94693661405,7102717.651746673 L 506247.3850936247,7103226.076707749 L 505701.52591707907,7101252.161370309 L 504917.91952391417,7098418.513527704 L 503889.7185733052,7093713.568902943 L 503459.5921222609,7088920.731138841 L 503991.78984373953,7084142.436695957 L 505891.4661057598,7079738.410866638 L 507548.8775404497,7075283.968200408 L 506903.82431188313,7070248.5143289035 L 506608.78163784184,7065166.419727904 L 506860.2930683734,7060070.36636984 L 506879.5949127314,7054968.063215779 L 507882.11752344825,7049988.6210904075 L 510292.0070854316,7045491.468786705 L 511703.41809331166,7040601.5031975955 L 512256.1736292842,7035529.516086775 L 511310.2056124447,7030562.423971243 L 511540.43050908623,7025540.353861195 L 512567.7921018769,7020812.562008069 L 513569.5460179676,7016078.52719805 L 513770.9020706098,7010701.271585709 L 513972.258123252,7005324.015973368 L 514173.6141758942,6999946.760361027 L 522365.6068846984,6999935.830635537 L 530557.5995935025,6999924.900910048 L 554348.8592647833,6999893.158701956 L 570732.8446823917,6999871.299250978 L 587116.8301,6999849.4398 L 582561.5661,7018674.8695 L 575869.2356,7036470.8903 L 576926.3259,7045703.28905 L 577983.4162,7054935.6878 L 588718.4379,7069551.7332 L 589991.551,7088916.2681 L 599216.6397,7101937.1833 L 597377.3083,7111529.7190000005 L 595537.9769,7121122.2547 L 595251.9884500001,7130979.33955 L 594966.0,7140836.4244 L 595434.2548,7158891.8944 L 589861.6203,7174975.0266 L 591874.6804,7184284.426750001 L 593887.7405,7193593.8269 L 583338.5222,7208961.1409 L 575857.9347,7227403.0768 L 570144.7264,7245079.2573 L 561720.352,7262020.3359 L 557571.5752,7270849.620200001 L 553422.7984,7279678.9045 L 544458.4262,7296257.6022 L 536187.0266,7313094.606 L 533666.216,7328135.9981 L 530177.8881,7344717.9132 L 532211.8707,7353280.47355 L 534245.8533,7361843.0339 L 545877.0319,7376454.3726 L 529573.9441387767,7378080.652234582 L 513270.85637755366,7379706.931869164 L 496967.7686163306,7381333.211503746 L 480664.68085510744,7382959.491138329 L 468312.95918970497,7384191.610703081 L 455961.23752430244,7385423.730267833 L 431257.7941934975,7387887.969397337 L 416930.3894801847,7389317.168997005 L 402602.9847668719,7390746.368596671 L 388275.5800535591,7392175.568196339 L 373948.1753402463,7393604.767796006 L 359620.7706269335,7395033.967395673 L 345293.3659136207,7396463.16699534 L 332941.64424821816,7397695.286560092 L 324790.1003676066,7398508.426377383 L 316638.5564869951,7399321.566194674 L 315540.38242775045,7394626.690214633 z M 496293.9269497144,7172795.7934198 L 496941.2415541466,7168398.953853622 L 497956.674798515,7163729.608127655 L 499558.9823280661,7159037.12912716 L 500413.6807124209,7154218.532016038 L 502550.9503822766,7149832.117641238 L 506572.3890594189,7147920.858049201 L 507757.19532564195,7152570.0828204695 L 506957.0904901547,7157531.501627015 L 507431.70875832305,7162513.343864531 L 508221.83267000766,7167443.607617017 L 509826.5968023008,7172205.904021375 L 511445.1309689946,7176963.158007601 L 513264.4093613919,7181660.047561348 L 514725.5201821907,7186462.881921106 L 516847.5874309929,7191024.715004624 L 518672.18567100674,7195722.487480154 L 520548.42660856765,7200399.795831556 L 522680.23199652205,7204964.0870271465 L 524383.4215153559,7209689.1046240125 L 526752.4608505724,7214098.203777843 L 528582.0739822089,7218793.823414772 L 530497.9488529272,7223454.634438367 L 532526.3347526877,7228065.62680842 L 534057.5398026314,7232844.95666782 L 533043.3583906292,7237540.933931722 L 529104.9668202852,7240417.980306625 L 527007.5656915765,7244997.970153048 L 525834.7257153634,7249891.929128957 L 525314.8048432942,7254902.595110269 L 525419.6573463796,7259939.123407484 L 525824.1361531824,7264959.151447965 L 524248.6083977852,7267007.425991014 L 520057.9473965844,7264426.9027731605 L 516720.0468862077,7260682.511521108 L 513832.0203658549,7256683.533039432 L 517913.87434652826,7253837.839522419 L 520869.76927324396,7249988.327769596 L 523230.8251948413,7245559.920788567 L 524569.3052548962,7240871.696910427 L 523541.0616146387,7235983.287721515 L 521308.13229068695,7231482.060776634 L 518394.45675144607,7227376.420029415 L 515306.6187999103,7223427.261244171 L 511536.16364109784,7220086.587284383 L 507563.1227883288,7216992.08171172 L 503362.44006036944,7214265.431507018 L 500970.19169302325,7210241.293166031 L 505013.22965000925,7209439.492736409 L 505022.35802689666,7204847.51641508 L 505554.83634459117,7200047.088715933 L 505337.35505339276,7195189.174292395 L 503140.0515634159,7190697.069898586 L 500241.6390646557,7186691.342486573 L 499725.72633784096,7181934.451599195 L 498542.78161356295,7177195.104699829 L 496293.9269497144,7172795.7934198 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x3f6098d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = m.mask()\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This provides a polygon covering the true `mesh` model domain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the desired grid resolution using the `dfsu` method `grid_res()`. This will calculate the weights required to interpolate from the unstructured mesh to a regular grid. This is the required initial step and once this is done multiple items of either element or node data is able to be interpolated on without re-calculating the weights. The weights will need to be re-calculated if the grid resolution changes.\n",
    "\n",
    "So for 500 metre spaced grid, call `grid_res` for 500 metres. We then create a boolean mask over the created grid using the `boolean_mask()` method; which will return `True` for points that are within model, and `False` otherwise. \n",
    "[*Note: This process is currently very inefficient and needs to be improved. Performance will become worse for higher resolution grids as more points are required to be checked*]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "area.grid_res(res=500)\n",
    "bm = area.boolean_mask(mask, res=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling `grid_res` creates the interpolation weights as well as `X` and `Y` grids at that resolution covering the model domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[283928.30655072, 284428.30655072, 284928.30655072, ...,\n",
       "         656428.30655072, 656928.30655072, 657428.30655072],\n",
       "        [283928.30655072, 284428.30655072, 284928.30655072, ...,\n",
       "         656428.30655072, 656928.30655072, 657428.30655072],\n",
       "        [283928.30655072, 284428.30655072, 284928.30655072, ...,\n",
       "         656428.30655072, 656928.30655072, 657428.30655072],\n",
       "        ...,\n",
       "        [283928.30655072, 284428.30655072, 284928.30655072, ...,\n",
       "         656428.30655072, 656928.30655072, 657428.30655072],\n",
       "        [283928.30655072, 284428.30655072, 284928.30655072, ...,\n",
       "         656428.30655072, 656928.30655072, 657428.30655072],\n",
       "        [283928.30655072, 284428.30655072, 284928.30655072, ...,\n",
       "         656428.30655072, 656928.30655072, 657428.30655072]]),\n",
       " array([[6999849.4398, 6999849.4398, 6999849.4398, ..., 6999849.4398,\n",
       "         6999849.4398, 6999849.4398],\n",
       "        [7000349.4398, 7000349.4398, 7000349.4398, ..., 7000349.4398,\n",
       "         7000349.4398, 7000349.4398],\n",
       "        [7000849.4398, 7000849.4398, 7000849.4398, ..., 7000849.4398,\n",
       "         7000849.4398, 7000849.4398],\n",
       "        ...,\n",
       "        [7497849.4398, 7497849.4398, 7497849.4398, ..., 7497849.4398,\n",
       "         7497849.4398, 7497849.4398],\n",
       "        [7498349.4398, 7498349.4398, 7498349.4398, ..., 7498349.4398,\n",
       "         7498349.4398, 7498349.4398],\n",
       "        [7498849.4398, 7498849.4398, 7498849.4398, ..., 7498849.4398,\n",
       "         7498849.4398, 7498849.4398]]))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.grid_x, area.grid_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "-----\n",
    "After creating the grid interpolation weights and the boolean mask, gridded data from the unstructured mesh can be obatined in a few ways:\n",
    "\n",
    "1. Call method `gridded_item` for specific item, such as surface elevation, for specific time stamp or range of time stamps\n",
    "2. Call method `gridded_stats` for specific item to get maximum or minimum item value across model run or within range of time stamps\n",
    "3. Call method `gridded_item` parsing in array for `node_data`, where the node data can be some manipulated data which can then be gridded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will only use method 1 as an example, but each approach is very similar for the others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "..\\dhitools\\_gridded_interpolate.py:107: RuntimeWarning: invalid value encountered in less\n",
      "  interp_z[np.any(weights < 0, axis=1)] = fill_value\n"
     ]
    }
   ],
   "source": [
    "grid_cs_t500 = area.gridded_item(item_name='Current speed', tstep_start=500, res=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply mask to gridded data and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply boolean mask using numpy masked array\n",
    "grid_cs_t500m = ma.masked_array(grid_cs_t500[0], mask = ~bm, fill_value=np.nan)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "ax.contourf(area.grid_x, area.grid_y, grid_cs_t500m)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have obtained current speed interpolated to a regular grid with a resolution of 500m. This can now be directly exported to a raster using `GDAL`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dev3",
   "language": "python",
   "name": "dev3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
